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How To Make A Mid-Career Move Into Data & Analytics

The world of work has changed and in the world of tech, it’s in a league all its own. While some articles ponder the strange strategy of school and work start and end times, others talk about the hottest jobs on the market. We already know Big Data, Analytics, and anything data-related is the place for aspiring data professionals. Both universities and businesses have amped up their curriculum and offers to help skilled workers keep up with the demand. Skilled workers. These are the people needed to fill the jobs of the future. The jobs automation can’t do, not yet. The jobs which require the human element like why a consumer buys one product or service over another (read: gut instinct). Gut instinct. It’s something developed through our human experiences. But what about those aspiring Data professionals who are coming from other industries and not directly from university. What would someone who wanted to make a mid-career change need to consider before making their move? 11 Things to Consider About Your Mid-Career Change While everyone has different reasons for changing career paths, two of the most common are money and unhappiness. For more about what employees want to remain in their job, check out this year’s Diversity Report. Mid-career changers making the leap into the tech industry plan for a year.  As you plan, think about these questions, and find your why. Then, figure out how. Are you looking for a more challenging role?Looking for advancement opportunities?Do you want more flexibility in your career?Do you just want to move location or do your motivations run deeper?Are you ready to be a student again? Are you willing to “learn the ropes” again – processes and frameworks?Do you have the support of your family as you enter this next phase of your life?What’s the best way for you to learn the technical skills required? Is it a university degree, or a combination of degree, certification program, and/or bootcamp?Are you prepared for the costs associated with a career-change? Not just moving from employee to student, but prepared for the right business for your newly acquired skills.Do you have what it takes to make your desired career change? Are you prepared for the challenges, pitfalls, and triumphs change and new projects brings? You’ve thought long and hard about these questions. Your family’s on board. You’ve made it through an intensive educational program, and you’re ready for next steps. So, now what? Well, we’ve got a few articles you might want to check out to learn best practices for applying in this industry. Though you may have an edge in your business knowledge, goals, processes, and implementation of technologies, there are a few other pieces we can offer to round things out. We want to help set you up for success with tips for your resume, including how to boost it with video, and add a layer of depth without shedding the basics. And to round it all out, check out our article on how to rock your interview.  Over to You  If you’re interested in making a career-change into the world of data, let us know how we did with this article. What other questions do you have? And if you’ve made the leap, we’d love to hear from you about your journey into Data Science or other position in the Data and Tech industry. If you’re interested in Big Data & Analytics, Bioinformatics, Computer Vision, or Data Science, we may have a role for you. Take a look at our current opportunities or get in touch with one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com

COMPUTER VISION AND MORE. WHAT’S IN STORE FOR 2020?

Computer Vision and More. What’s in Store for 2020?

For years, we’ve been blending tech with tradition as technological advances have moved us forward. At the heart of it all are Data, the Internet of Things (IoT) and the Industrial Internet of Things (IIoT). Demand is high for advanced connected devices and at the industrial level, this means robots. According to market reports, the smart robot market is expected to be worth just over $14 billion by 2023. As we reach the turn of the decade, there are a few other trends to be mindful of as well. These include such disruptive technologies as automation, phygital spaces, smart buildings, and digital twinning to name a few. While disruption may often be evocative of something negative. In the tech world, it isn’t. But how the technologies are used and what they’re for may change our world, not unlike the invention of electricity changed the world of agriculture into the Industrial Age. With that in mind, here are some of the most disruptive technologies for 2020: Robots & Automation It’s almost a broken record, isn’t it? How quickly technological advances are marching across our landscape of connected devices? But the immersion of these devices into our lives offer a variety of interactions far beyond that we’ve so far imagined. AI-enabled robots, for example, are able to interact and respond to time crunched human situations. There’s more to robots and automation than meets the eye and the additional technologies just might have the answers to the challenges we are facing and will face in the future. Phygital Spaces  What if you could go to a baseball game with the experience of being at the game without leaving your couch? What if you could watch a race as though you were a participant? What if…? What if the technology to do this was already here? Enter phygital spaces, the blending of physical spaces and digital technology by bringing together AR, VR, mixed, and human reality.  Ready, Player One? Predictions are in for the growth of the AR/VR Industry to a $160 billion industry in the next three years.  Smart buildings  Since 2000, the smart building market has been expanding. Voice Assistants, smart home tech, and IoT allow you to check on your home’s safety from a remote location, control your temperatures, and even let the cable man in (if you still have it) without ever leaving your office.  However, immersive experiences are also becoming part of business management systems as well. The common denominating factor in these new advances? Where once you controlled each stage, now, based on preferences, changes will be made by measuring heat signatures, time of day, or some other assigned metric.   Safety & Security Come First As exciting as these advances are, there is one important thing to remember. While robots have become smarter and we offer an abundance of Data to varying degrees for our convenience, robots are not human. They may be able to reason in bits and bytes, but moral reasoning remains an entirely human endeavor. GDPR in the UK. Data Privacy Regulations state by state across the United States. They are steps in ensuring your Data’s safety and security, but as we increasingly combine and blend robots and automation, AR and VR technologies, digital twinning, voice assistants, and more across our connected devices its important to be aware and careful what you do on networked systems. No matter how strong your password, no matter how secure your system, everything can be hacked. Want to get a jump on your 2020 job search? If you’re interested in Computer Vision, Robots and Automation, Big Data and Digital or Web Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Privacy Is Big Business For Small And Mid-Size Businesses

Privacy is Big Business for Small and Mid-Size Businesses

If you’re a small to mid-size business and think cyber criminals only go after big business; think again. It’s just as important, if not more important for you to have privacy plans in place. This goes way beyond GDPR and state-to-state rules, this is about how you care for your customers personal information. The return on investment will set the tone for future years of your business. After all, according to a 2018 report by Verizon, 58% of cyber-attacks targeted small business. While it may seem counter-intuitive and larger businesses are bigger fish to go after, they can be difficult to get into. After all, they’ve got the resources to protect their customer’s Data and are hyper aware of what it can be to their business if they don’t. Smaller and mid-size businesses generally don’t have the resources of the larger businesses, and may not focus on cybersecurity like they should which leaves their business wide open for cybercriminals. Chinks in the Armor of Your Data Cybercriminals excel at finding “chinks in the armor” of your Data. They’ll use any advantage to break in from the usual hacking and malware to physical breaches. One improperly secured device can be just the entry they need into your entire system.  What can you do? Be focused in your approach to Data security. Many small businesses tend to put out fires, rather than have a focused strategy. And each approach to tighten security can lead to more opportunity for hacking.Communicate your strategy to every member of your team. Something as small as clicking on the wrong link can lead to a Data breach.Train your staff on measures they can take such as to not click on a link they’re not expecting, to check email addresses and ensure they’re approved or white-listed as okay to access. The more aware your staff are, the better able they’ll be able to help ensure the security of your business’ Data. While staff may be on the front lines, this also requires a commitment from senior executives as well. Understand that just because you’re not dealing in billions of dollars, you may actually be at greater risk. Why? Because unlike the larger companies, your business may not survive the fallout of a cyber-attack. How to Protect Your SMB You can protect your business by creating a Data Security Strategy and consider the following: Encrypt your data;Authenticate your users by either a 2-step verification process or having them enter some kind of code;Authorize access to trusted sources. Encrypting Data helps protect the private and sensitive information and makes it unreadable without the correct key. To ensure only those who are trusted sources have access is through authentication.  Authentication can include username/password, code, tokens, phone number, and image association such as click only the boxes with pictures of street lights or stop signs. This helps your business control who has access and gives you tighter rein over who sees sensitive information and what they can do with it.  By defining the rules and regulations of access to information, training your employees to be aware and what to do to ensure security, you can strike a balance of increased security and transparency to your customers. In other words, the efforts you go through to protect their Data will put you ahead of the competition as you make inroads toward a Data privacy strategy while others take action as things happen. One Final Thought Ensuring your business’ Data is protected and detecting times when it may have been breached is increasingly important to help minimize damage. One issue SMBs face is that it may take longer to detect if there isn’t a Data security plan in place. The more quickly you can detect an issue, the more quickly you can reduce its impact and the more quickly and effectively you can respond, the better.  Interestingly, smaller businesses tend to have a better overall picture of their assets than larger businesses. This can be a boon when you communicate your new cybersecurity strategy to your teams and offers a significant return on investment of your resources. If you’re interested in Big Data and Analytics, we may have a role for you. Take a look at our current vacancies or contact one of our recruitment consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Is Bioinformatics The Next “Rock Star” In Data?

Is Bioinformatics the Next “Rock Star” in Data?

It’s open enrolment for healthcare here in the US with a maze of plans to choose from. If you want to dip your toes into the world of healthcare with a tech bent, you may want to check out Bioinformatics or health informatics, and yes, there is a difference.  Bioinformatics is a growing field and is expected to grow to $16 billion by the 2022. It may just be the next “rock star” profession for those in the Data & Analytics fields. So, what is Bioinformatics and how is it different from Health Informatics? What is Bioinformatics? It’s the marriage of biology and information technology. In a world constantly on the go, and as we grow older and live longer, it helps us find the answers we seek. Bioinformatics often begins at the beginning. Think genome research, for a start. Yet, ultimately, it focuses on biological data in medical research and drug development. Imagine collecting and organizing data to annotate, record, analyze, and extract structural information in relation to protein sequences or applying your knowledge to chromosome therapy, drug innovations, or forensic analysis.  Because of the advances in IT, what was once unimaginable is now available. A booming industry which is a boon to the population. House, M.D. meets Bones.  Within this industry are sub-categories and sub-applications. In other words, there’s something for everyone interested in both biology and computer science. Here’s a quick list: Medical BiotechnologyAnimal BiotechnologyAcademicsAgricultureForensicsEnvironmental And within these sectors, though not the full list, their applications: GenomicsChemoinformaticsDrug designTransciptomics What is Health Informatics? Health Informatics is similar to Bioinformatics in that it uses computer technology to further advancements in medicine. However, while Bioinformatics focuses on the biology side of things, Health Informatics (HI) is focused on the patient side; helping doctors and patients determine care. HI is the application of design, development, and analysis of patient and healthcare Data systems. It’s the nervous system equivalent of a hospital or doctor’s office which houses medical records, billing systems, and compliance systems. For those with a computer science background who are more interested in the information infrastructure and architecture of a healthcare enterprise, Health Informatics may be for you. If you’re interested in the administration side of healthcare, you may want to think about Health Information Management (HIM). You can also learn more, here. Getting Your Foot in the Door You know the basics. Have a technical background with the communication skills to explain your findings. Boost your resume with video. Have done a project or two to show your work and capabilities, but when you drill down to something like informatics, there’s one more bit of training you’ll want to have. Since Bioinformatics, for example, is the marriage of biology and technology, it’s important to have a background in molecular biology and computer science. Drill down further and you’ll want to include database design as well. The Sum of its Parts Bioinformatics is an emerging science, in which we develop and use computer databases to enhance our biological research. Analyzing, storing, managing the data we collect or extract; this is the sum of its parts. Advancements here give us the opportunity to more efficiently identify new therapies, new treatments, new sequences to better understand disease. The potential to improve personalized medicine is exponential. What we learn and find today may help us solve tomorrow’s healthcare issues.  Want to get in on this growing healthcare field and the next generation of IT? Interested in Big Data and Analytics, but not necessarily the healthcare industry. We’ve got you covered. We specialize in Junior and Senior roles. We may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Tips for your Data & Analytics Resume

Tips for your Data & Analytics Resume

So, you’re pursuing a career in Data & Analytics. The brilliant thing about this is you’re entering a fast-growing industry with the potential for a great salary. But, unfortunately, this also means you’re probably entering into one of the most competitive fields out there right now.  The question is, how can you ensure your resume stands out from the crowd and impresses any potential employer?  Here are some top tips to help boost your Data & Analytics resume. Formatting is important It may seem obvious, but handing over a messy resume with no headings and massive blocks of text is no way to make a good first impression. Research suggests your resume is only looked at for a total of six seconds, so it’s important to make an impact on first glance.  Not only does this entail creating a well-presented document overall, but it also means paying attention to the small details such as structuring your resume to best emphasise the qualities and experience you think speak most highly of your ability to do the job well. This is why utilising a reverse chronological format is sometimes a worthwhile idea. For a highly competitive job in a Data & Analytics related field, where past experience is an important factor, beginning a resume with your most recent experience nearest the top will draw the eye and attention of the hiring manager reading it. Additionally, make sure your skills, qualifications, extra courses and impressive achievements are highlighted and clearly stated within the main body. As such, it’s better to use bullet points wherever possible instead of paragraphs and, consequently, you’ll find your resume a lot more compact and legible; in other words, much more likely to be read and remembered.  Quality over quantity  Having the most aesthetically pleasing resume in the world will mean nothing if the content doesn’t relate to the job you’re applying for. Again, this may sound obvious but it’s always worth combing through your resume to eliminate any irrelevant features and leave more space to talk about the things that matter.  Having a single page summarizing the most impressive contributions in your last role, or the most valuable insights gathered from a particular project you were involved with, is much more valuable than a multi-page essay about your volunteering with a local soccer club five years ago (unless, of course, your role heavily related to Data & Analytics). When introducing yourself, avoid long sentences and pronouns, and use impactful verbs when describing your achievements: for instance, try “instigated” instead of “started” and “spearheaded” instead of “led”. Also be sure to highlight and, where possible, quantify how your previous work in data/analytics benefitted your old company.  Know the value of your skillset It’s worth dedicating a section of your resume just to listing your most valuable skills as they relate to the job you want. However, make sure to be specific when describing your technical skills and experience with whichever tool you’re talking about. State your level of expertise and how you utilized said software to make your knowledge clear to whoever’s reading.  If you’re applying for an entry level position, however, and don’t have much experience or technical skills yet, it’s important to show off whichever skills you already have and how they  will make you a great addition. It’s worth researching which of your more general skills are the most sought after by employers, and then gaining an understanding of which ones best relate to the job you’re trying to get. For jobs working in Data Science, for instance, maths skills, analytical skills and problem solving are well worth mentioning. Ultimately, you want this section to contain a comprehensive, impressive sounding, and accurate, list of your most relevant skills.   If you’re interested in Big Data & Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to find out more:  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com. This guest blog was provided by check-a-salary. 

Big Data In Politics – Win, Lose, Or Draw

Big Data In Politics – Win, Lose, Or Draw

In the movie Definitely, Maybe starring Ryan Reynolds, there’s a scene in which he must sell tables for a political campaign dinner fundraiser. He makes call after call with no luck. Finally, in frustration, he speaks plainly and finds a connection between the politician and the prospective donor. In an instant, he understands. Make the connection and you can’t go wrong. This is the 90’s version of micro-targeting. Online advertising today has honed targeted Marketing to an art form and it’s infused every industry from Fisherman’s Wharf to Wall Street to Washington. Messages are crafted on detailed profiles of what makes us unique such as hopes, fears, dreams, emotional triggers, and more which is then taken out of the hands of humans. Enter such deep, personal details into automated technologies and you’ll get automated reactions. How did we get here? Ever since Cicero’s brother, Quintus, who approached politics with a do anything to win mindset, we’ve been working toward this point. But, when it comes to technological advances within politics, George Simmel put it best when he wrote around 1915, “the vast intensive and extensive growth of our technology…entangles us in a web of means, and means toward means, more and more intermediate stages, causing us to lose sight of our real ultimate ends.”  What does this mean? It means we have moved so quickly and with such intensity as we push inwards while reaching outward, we get tangled up in our own systems. Before we know it, it’s difficult to separate the means from their ends, and we lose sight of our purpose. In other words, it can be hard to keep our sense of direction with our constant distraction of tasks, systems, and processes. According to Simmel, this would soon morph into what he called a ‘fragmentary character.’ Like a mosaic, we put the pieces back together and assemble the bits to fit our concept of the world.   The Digitizing of Campaigns Traditional campaigning has traditionally looked much like the movie scene mentioned above with phone banks, whiteboards, and handmade signs. But, today, things are changing. Everyone has at least one smart device which can sync information in real time to a range of devices. Algorithms and predictive modeling help reduce the guesswork, though gut feeling and instinct still prevail. At least, for now. Our machines are learning how to learn about us and define what we believe and wish to see by historical Data, or rather our past behaviors. Where psychographic profiling meets micro-targeting. What was once only seen in the Marketing world has now entered politics. Just like marketers want to know what people are interested in, so to do politicians wish to know what voters think. To do this, both industries will study behavioral and attitudinal profiles to help understand a demographic better or discern a gap in the marketplace. In consumer research, companies rely on psychographic micro-targeting to reach smaller groups and individuals. The key question here is to ask is to what extent are politicians prepared to pass laws that restrict their own opportunities to know more about voters. Just as the next generation of voters are coming, so too are the next generation of tools being developed.  One Final Thought… Over the last 20 years or so, we have built an immense Data structure from mobile devices to social media to modelling processes and more. With this kind of connectivity combined with fragmentary media, the use of Data Analysis has a big role to play going forward. If we seek change in our political and social infrastructures, we will have to reimagine the structures currently in place. From algorithmic modelling to AI and Machine Learning, the possibilities for new ideologies has emerged blurring the lines between context and production in which Data underpins capitalism. As those in Data Analytics continue to pursue an uninterrupted (read: non-fragmentary) vision of the world, we find ourselves at a new stage in history of where both looking back and looking forward at the same time informs our future.   Where would you like to go? If you’re interested in Big Data & Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to find out more:  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Going Green With Big Data

Going Green With Big Data

Greta Thunberg sailed the Atlantic to come the UN to talk about climate change. Her mother, a renowned opera singer, has given up air travel to support her daughter’s efforts. There is a zero-waste movement to lessen our trash and help alleviate the carbon footprints from our buying, traveling and more. These are steps humans have made. Yet technological advances may make it possible to flip the script for the environment and Big Data has a big role to play.   What are Some of the Advances Taking Place? Technological advances have brought us breakthroughs in modern science and in every industry. Now, we are at a time and place in where our technologies cam help tackle climate change. From modeling to predictions, we can begin to build not just a map of environmental concerns, but begin to build a road toward a solution. Below are just a few of the ways technology is being used to advance solutions for climate change. AI modeling makes it easier to identify problemsPredictive Analytics models can create different scenarios to see ‘what happens if?’Big Data is used to identify areas which need immediate attention This is just the tip of the iceberg when it comes to using technology to predict and identify climate concerns. While some parts of the world contribute more to the problem than others, Big Data has made it possible to draw conclusions where the hardest hit areas are and is key to addressing the problem. But whatever Data brings, the information is useless if it isn’t used to formulate and put forward better environmental practices and policies.  Ways to Upscale Urban Data Science  Manhattan, Berlin, and New Delhi, as varied as they are, have one thing in common. They’re often sites for case studies when it comes to analyzing our environment. However, our advances continue to improve and we’re able to learn from state-of-the-art Data infrastructures. These can include such things as social media data combined with earth observations to see how they might better integrate. A research publication in Berlin suggest three routes for expanding knowledge. They are: Mainstream Data collectionsAmplify Big Data and Machine Learning to scale solutions and maintain privacyUse computational methods to analyze qualitative Data With these advances in place, there is a chance urban climate solutions could effect change on a global scale. With the proper Data of urban areas in place, including that of related greenhouse gases, socio-economic issues, and climate threats, Data professionals can get a clearer picture of what needs to be done. Building on the advances that are in place with the integrated technologies of AI, Predictive Analytics, and Big Data helps make big strides in combatting climate change. According to reports, only about 100 cities make up 20% of the global carbon footprint. Yet 97% of climate concerns are focused in urban areas. There’s still a lot which remains to be done to combat the greatest issue of our age, but working hand in hand – machine and human – we just might find ourselves on reprieve and the chance to leave the world better than we found it for the next generation. The next Greta Thunbergs of the world. If you’re interested in Big Data & Analytics, we may have a role for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Data Engineer Or Software Engineer: What Does Your Business Need?

We are in a time in which what we do with Data matters. Over the last few years, we have seen a rapid rise in the number of Data Scientists and Machine Learning Engineers as businesses look to find deeper insights and improve their strategies. But, without proper access to the right Data that has been processed and massaged, Data Scientists and Machine Learning Engineers would be unable to do their job properly.   So who are the people who work in the background and are responsible to make sure all of this works? The quick answer is Data Engineers!... or is it? In reality, there are two similar, yet different profiles who can help help a company achieve their Data-driven goals.  Data Engineers  When people think of Data Engineers, they think of people who make Data more accessible to others within an organization. Their responsibility is to make sure the end user of the Data, whether it be an Analyst, Data Scientist, or an executive, can get accurate Data from which the business can make insightful decisions. They are experts when it comes to data modeling, often working with SQL.  Frequently, “modern” Data Engineers work with a number of tools including Spark, Kafka, and AWS (or any cloud provider), whilst some newer Databases/Data Warehouses include Mongo DB and Snowflake. Companies are choosing to leverage these technologies and update their stack because it allows Data teams to move at a much faster pace and be able to deliver results to their stakeholders.   An enterprise looking for a Data Engineer will need someone to focus more on their Data Warehouse and utilize their strong knowledge of querying information, whilst constantly working to ingest/process Data. Data Engineers also focus more on Data Flow and knowing how each Data sets works in collaboration with one another.    Software Engineers - Data Similar to a Data Engineers, Software Engineers - Data ( who I will refer to as Software Data Engineers in this article) also build out Data Pipelines. These individuals might go by different names like Platform or Infrastructure Engineer. They have to be good with SQL and Data Modeling, working with similar technologies such as Spark, AWS, and Hadoop. What separates Software Data Engineers from Data Engineers is the necessity to look at things from a macro-level. They are responsible for building out the cluster manager and scheduler, the distributed cluster system, and implementing code to make things function faster and more efficiently.  Software Data Engineers are also better programers. Frequently, they will work in Python, Java, Scala, and more recently, Golang. They also work with DevOps tools such as Docker, Kubernetes, or some sort of CI/CD tool like Jenkins. These skills are critical as Software Data Engineers are constantly testing and deploying new services to make systems more efficient.   This is important to understand, especially when incorporating Data Science and Machine Learning teams. If Data Scientists or Machine Learning Engineers do not have a strong Software Engineers in place to build their platforms, the models they build won’t be fully maximized. They also have to be able to scale out systems as their platform grows in order to handle more Data, while finding ways to make improvements. Software Data Engineers will also be looking to work with Data Scientists and Machine Learning Engineers in order to understand the prerequisites of what is needed to support a Machine Learning model.   Which is right for your business?  If you are looking for someone who can focus extensively on pulling Data from a Data source or API, before transforming or “massaging” the Data, and then moving it elsewhere, then you are looking for a Data Engineer. Quality Data Engineers will be really good at querying Data and Data Modeling and will also be good at working with Data Warehouses and using visualization tools like Tableau or Looker.   If you need someone who can wear multiple hats and build highly scalable and distributed systems, you are looking for a Software Data Engineer. It's more common to see this role in smaller companies and teams, since Hiring Managers often need someone who can do multiple tasks due to budget constraints and the need for a leaner team. They will also be better coders and have some experience working with DevOps tools. Although they might be able to do more than a Data Engineer, Software Data Engineers may not be as strong when it comes to the nitty gritty parts of Data Engineering, in particular querying Data and working within a Data Warehouse.  It is always a challenge knowing which type of job to recruit for. It is not uncommon to see job posts where companies advertise that they are looking for a Data Engineer, but in reality are looking for a Software Data Engineer or Machine Learning Platform Engineer. In order to bring the right candidates to your door, it is crucial to have an understanding of what responsibilities you are looking to be fulfilled. That's not to say a Data Engineer can't work with Docker or Kubernetes. Engineers are working in a time where they need to become proficient with multiple tools and be constantly honing their skills to keep up with the competition. However, it is this demand to keep up with the latest tech trends and choices that makes finding the right candidate difficult. Hiring Managers need to identify which skills are essential for the role from the start, and which can be easily picked up on the job. Hiring teams should focus on an individual's past experience and the projects they have worked on, rather than looking at their previous job titles.  If you're looking to hire a Data Engineer or a Software Data Engineer, or to find a new role in this area, we may be able to help.  Take a look at our latest opportunities or get in touch if you have any questions. 

Sean Byrnes, CEO Of Outlier.Ai, On Creating A Business With Values

Sean Byrnes, CEO Of Outlier.Ai, On Creating A Business With Values

We sat down with Sean Byrnes, CEO of Outlier, an Analytics solution provider, to learn more about Outlier and its values. He also shared us with the power a candidate has when applying and interviewing for jobs in the tech industry today as well as how employers can retain their top talent. How long has Outlier been in business? We’ve been in business about four years. Prior to starting Outlier, I had another company called Flurry which we sold in 2014. When we sold it, I took a year off to reset my internal counter. I needed to reorient my work/life balance and then, when I felt I was in good shape to get back into things, we kicked off the launch of Outlier.  When you began Outlier, did you plan to map out your values of diversity, inclusion, transparency, and work/life balance? Or did it evolve as the company grew? Planning out our values is one of the first things my co-founder, Mike Kim, did when we were starting the company. It was intentional. We sat down and wrote down those values you see on the website.   I learned a lot in my previous company and there were a lot of things I did which followed the values we have which we didn’t follow intentionally. It just felt…right. Growing up in New York, a very diverse place, having a diverse team always felt more natural to me than a homogenous team. Add to that, having just spent a year with my new daughter and adjusting to being a parent and what that meant; it put things in perspective.  If there’s one thing I’ve learned it’s this. There’s no reward for running a company well. There’s no reward for following good practices and treating your employers with respect. In fact, the reason I started my first company was that I’d worked a lot of places that treated employers like resources or like widgets.  You put salary in one side and productivity comes out the other. I wanted to work somewhere that treated people like people. So, I started Flurry, and now have come into Outlier with a solid idea of our values and company culture. As important as tech skills are, there’s no data to support a feedback mechanism and predict success. Success comes from unexpected places.  So, by sitting down and writing out those values, we weren’t just signing up for a contract for how we would treat the people.  In our way, we were standing up as an example of how you can build a tech company that didn’t follow these bad habits.   Someone said, not too long ago, that the best employees stay in the same company for about 20-months before moving on to their next project. Often, it’s because the employee no longer feels creative.  In thinking through your company values statements, what would you say to a business who’s hoping to both attract and retain their top talent? Or do you think it’s better for these individuals to rotate off in order to keep things moving?  Tech companies typically have two problems: Companies are so desperate to keep going, they do whatever they can to survive. They promise themselves they’ll make compromises and fix the short cuts when the time is right.  So, they hire people who need not share their values or may not meet their criteria with a promise that when the company is successful, they will fix it and that time never comes. You’re never at a point where you’re so successful you can go back and remake those mistakes or fix them.For many, recruiting is a one-way system. You search for and hire a candidate, then another, and another without really putting much thought into it.  The reality is that you can’t build a high growth business that way. What you have to believe is that if you spend the time to find the great people that enjoy working on what you working on, where you treat them with respect, you give them not just responsibility but also the authority to do things that you create gravity. You create a world where the people you hire pull in the next group of people because people really want to work them, they want to be in that environment. A lot of our most recent hires here at Outlier are people that came to us, who wanted to work in this environment and when they saw how great our team are they want to add to that and it becomes a self-fulfilling cycle where the larger your core of great people the better your gravity is, the more great people it pulls in and so the gravity needs to expand and that’s how you build a high grow organization. You don’t build a high growth organization by having the best sourcing process, by having the largest recruiting team, the best employ onboarding. You grow the fastest if you can create a community and an environment people want to work in and when it becomes self-evident of that then you start to pull people in. And so those become the culmination.  How do the values that you’ve initiated affect your company? Have you found that those values foster a deeper loyalty and higher moral than in other places and other business that sound a little bit like yours?  We have a very low attrition rate. Yeah, people have left due to life changes, but otherwise we still have the same people we started with though their roles may have changed as we grew. There was nothing we set out to do, nothing on purpose to keep them, but just did things which felt right. In retrospect, it comes down to this. If you create an environment where people are learning, where they feel valued, and they enjoy the work, why would they leave? Where would you go? What could you prefer to that environment? I think by focusing on this early, it’s led to more employee retention.  My hope is that everybody’s who’s here will continue along the journey as we build the company together. Another example is our work/life balance. Both my co-founder and I are parents of young kids. My first few hires were parents of young kids. So, being family friendly has always been a core value of ours from the beginning.   Being family friendly is both a conceptual and a practical concept. Our policy is to be in the office three days per week and work from home two days per week. This gives our parents a chance to do things like take their kids to the doctor or attend a parent/teacher meeting. It’s become an enormously strong aspect for us because there’s a lot of people who have young kids and they don’t want to work in your stereotypical tech company. You know the one, the company who expects you to be in the office for twelve hours a day seven days a week.   Our employees like the challenge of building tech companies, but they need the flexibility we offer, too. So, just by having a simple stand of valuing people as people and parents as parents, means we have a competitive advantage in recruiting for roles. The senior people who can contribute at a very high level where they suddenly didn’t have any options and this is a chance for them to keep doing what they doing. They’re able to do what they love and not feel they have to compromise their family on its behalf.   It’s the kind of thing where very simple principles becomes an enormously strong competitive strong advantage in todays market. While it hasn’t been true for very long, it has definitely been the case so far for us. If you’re interested in Big Data and Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

MACHINE LEARNING ENTERS BIOINFORMATICS AND ITS FUTURE IS BRIGHT

Machine Learning Enters Bioinformatics and its Future is Bright

Ever wondered how your email system knows which emails to show you and which to put in your junk or spam folder? Enter Machine Learning. It learns what you open and read and after a time can differentiate what you ignore, toss, or move to spam. Now imagine that same type of learning in the life sciences. As scientific advances move toward Data and Machine Learning to scale their knowledge, you can imagine the possibilities. After all, as you read this, trends in the life sciences, specifically with an eye toward bioinformatics showcase machine learning such as genome sequencing and the evolutionary of tree structures. Human and Machine Learning with a Common Goal There has been so much data provided over the past few decades, no mere mortal could possibly collect and analyze it all. It is beyond the ability of human researchers to effectively examine and process such massive amounts of information without a computer’s help.  So, machines must learn the algorithms and they do so in any number of ways. For the most part, it’s a comparison of what we know, or is already in a databank, with the information we have and don’t yet know. Unrecognized genes are identified by machines taught their function. The Future is Bright Machine Learning is giving other fields within the life sciences both roots and wings.  Imagine scientists being able to gain insight and learn from early detection predictions. This type of knowledge is already in play using neuroimaging techniques for CT and MRI capabilities. This is useful on a number of levels, not the least of which is in brain function; think Alzheimer’s Research, for example.  The hurdle? It isn’t the availability of such vast amounts of data, but the available computing resources. Add to that, humans will be the ones to check and counter-check validity which can in turn become more time-consuming and labor intensive than the computer’s original analysis. And it’s this hurdle which leads to a caveat emptor, or “buyer beware” of sorts. Caveat Emptor: Continue to Question Your Predictions In other words, how much can you trust the discoveries made using Machine Learning techniques in bioinformatics? The answer? Never assume. Always double check. Verify. But as you do so, know this. Work is already in progress for next-generation systems which can assess their own work.  Some discoveries cannot be reproduced. Why? Sometimes it’s more about asking the right question. Currently, a machine might look at two different clusters of data and see that they’re completely different. Rather than state the differences, we’re still working on a system that has the machine asking a different kind of question. You might think of it as a more human question that goes a bit deeper.  Imagine a machine that might say something noting the fact that some of the data is grouped together, but if different, it might say while it sees similarities, but am uncertain about these other groups of data. They’re not quite the same, but they’re close.  Machine Learning is intended to learn from itself, from its users, and from its predictions. Though a branch of statistics and computer science, it isn’t held to following explicit instructions. Like humans, it learns from data albeit at a much faster rate of speed. And its possibilities are only getting started. Want to see where Bioinformatics can take your career? We may have a role for you. If you’re interested in Big Data and Analytics, take a look at our our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

DATA SCIENTISTS MOVE THE NEEDLE TO SCALE BUSINESS

Data Scientists Move the Needle to Scale Business

Today’s companies know how important it is to add Machine Learning and AI into their business, but without a plan, things can easily go sideways. Hiring Data Scientists for your business involves more than just hoping for the next Facebook or Google success. It’s about moving the needle on your own business. So, how do you do that?  Well, first you’ll want to think about what is you want from your business and from there, build a team to help get you there. We know that sometimes these two things pose challenges as well. So, where should you should begin? Ensure you have the right leadership structures in place In their role as Stakeholder, it’s important for top executives to have a clear understanding of where their business is and solid framework for where they want it to go. In other words, it’s important to ask yourself, is your business ready for that sort of growth and transition? Before you say, yes, and start looking for that unicorn employee, there’s a few things you need to know. Data Scientists aren’t magicians. They cannot wave a magic wand and make you ten times profitable overnight. Efficiency will come, but it will take time. In reality, if you don’t have the right Data sets, the right people in place, or the right backing and investment, then it will be a hard road to success and can lead to failed initiatives. So, how do you avoid that and get yourself set back on the right path? Here are a few key steps to consider: Don’t Put the Cart Before the Horse Understand your focus and the why of your business. Align your teams with a clear view of where you are and where you’d like to go in your business. Set clear expectations for yourself and your team.Decide What Your Team Should Look Like Ask yourself, “How much talent do I need?” Well, that depends on how much Data you’ll be working with and where you want those initiatives directed. For example, if you’re building a team just to focus on work recommendation systems, then you’ll need a far smaller team than if you were overhauling an entire platform or product line. Stop Chasing Shiny Objects Be realistic in your expectations as you build your team. So many businesses, when they think of Data Scientist, focus on the word scientist. And their first thought is they’d like to get someone with a PhD from Stanford who’s worked ten years at Google. A couple of things come to mind here, when I hear this and the first is this: When businesses first reach out, they talk to me about how they want someone from Google or Facebook or Netflix, but the reality is 9 times out of 10, you’re not going to be able to access that kind of talent.Be realistic about what sort of talent you can gain access to and ask yourself, if you could get someone from Google, Facebook, or Netflix, why would they leave that job to come work for you? What can you offer that those businesses cannot? It's important to understand the goal here is not to chase the shiny stereotypes, but to have clarity and desire to set up for success those that you do hire. For some businesses, the initial reaction is to just throw Data Scientists at a problem and believe they can fix things or move you forward faster. But like everything you need to put steps in place to get you from where you are now to where you want to be.  Learn, Grow, Pivot For most people who don’t come from a Data Scientist background, there are two schools of thought. Data Scientists are bright shiny objects who will fix all of their business problems overnight orA waste of time. To build a successful team, begin by educating those in the dark about what Data professionals are capable of and ensure everyone is aware of what is realistic. It’s important to understand that it may take six months to a year for a business to see any real outcomes. This doesn’t mean things aren’t working. It’s about investing time and not getting itchy, if something doesn’t instantly showcase results.  Rethinking Stability The gold watch after 40 years of working for the same company is a thing of the past. Good Data Scientists today, typically stay on for about 20-months, then move on to their next creative endeavor. This can be scary for a world that expects people to stay in positions for four or more years, particularly if you’re not from a tech background. However, the definition of a scientist is someone who researchers, someone who tries to find new ideas and new concepts, so these are people who are naturally inclined towards learning and towards being in new situations and these people get very, very bored very quickly. Understand that if you build your team well, people will move on and drop away. This is a good thing. It means you built a strong team and the ones who have moved on have got you to this place, now you need the next team to help you get to the next level. A constant state of flux is scary, but it can also mean your business is scaling faster, and your team of Data professionals are doing their job to move the needle. Give your team the freedom to learn, give the freedom to work on projects outside of their natural scope to be able to bring value to your business, although even within that year 18-month framework you might not see proof straight away. You need to be ok with the fact that you going to lose people. That typically means you’re doing something good.  If you’ve got a Data Scientist who is happy to just stay at your company and work on the same projects for six, seven, eight years, that’s probably a red flag; how will they keep improving? “What Got Us Here, Won’t Get Us There” The needs of your group are going to change and shift and so the people you need are going to change and shift. Again, it’s about being adaptable and being able to ride those waves and change as and when we need to. If you’re looking for more guidance in scaling your business using Data Scientists and building your Data team, Harnham can help. If you’re a candidate interested in Big Data & Analytics, we may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Web Analytics Trends

Web Analytics Experts On The Year’s Biggest Trends

Size. Scale. Strategy. Metrics. The measurement of the customer experience is inherent throughout our marketing and advertising efforts in today’s world. So, we thought we’d take a step back and ask Data professionals about their thoughts on current and upcoming trends in the realm of Web Analytics.Their answers were as wide and varied as the people who gave them. But to a one, these are all in executive leadership and are exclusively focused on Web Analytics and its effects on consumer behavior. Personalization Gets More Personal  "With IoT, today service providers have a huge amount of data about their consumers. In Web Analytics, this has led to analysis of individual customer behavior, which is enabling companies to offer personalized services to their customers.  Therefore, on-page analysis has become popular because the aim here is to convert the visitor to a purchaser when the opportunity presents itself. With voice search on the rise, this has led to a push towards analyzing voices- so as to understand emotions and identify points of frustration which lead to abandoned carts."  - Avinash Chandra, Founder and CEO at BrandLoom  Privacy Focus  “Many people are concerned about how the likes of Google and Facebook handle the analytics data they collect. Google Analytics has been the default choice for most tech teams for years now, but it's being installed on fewer and fewer sites nowadays. Instead of handing your website's visitors' data to Google, many young companies want to be in control of their visitors' data.”  - Uku Tehrat, CEO at Plausible Insights Reading the Tea Leaves of Web Analytics “Access to deep information has a drawback; it’s made us reliant on the instantaneous raw data from each analytics channel to inform strategy. However, that data isn’t always accurate. To solve the problem, marketers and analysts will need to take a more holistic view of the data.  Instead of, for example, tracking revenue by channel solely in analytics, the analytics professional will be looking at the real revenue of the business and evaluating the changes in the marketing that have led to that result. By looking at the real business KPIs and creating narratives that reflect the whole of the data, an analyst will be able to avoid the short-term thinking that comes from the constant analysis of daily analytics reports.” - Doug R Thomas, Marketing Consultant at Magniventris  The Third Wave of Business Intelligence  Sean Byrnes, the CEO of Outlier, an analytics solution provider, is seeing an interesting shift in Data Analytics. This shift has us entering the third wave of Business Intelligence (BI).  The first wave, data centralization, aggregated business data in a single place, making it easy to know where to look for answers. Next, data visualization tools made extracting answers easy and accessible, allowing anyone in the organization to make use of them.  The third wave is Automated Analysis. This wave will have a big impact on data scientists and how they do their jobs. In this third wave of BI innovation, automated analysis systems will constantly examine all of a business' data and provide "curated" insights related to specific and actionable changes in the business. This is vastly different than today's dashboards, giving data scientists specific, daily direction on which parts of the business to focus on. This also helps data scientists elevate their own brand to one of a data counselor. Helping define how to use data insights to fine-tune the business.  - Sean Byrnes, CEO at Outlier. Video Marketing is Here to Stay “From a marketing perspective, the biggest trend I see moving forward is the continual rise of video consumption.  Consumers are shifting from reading blogposts to watching YouTube videos, and from reading books to watching Netflix. We know that the currency of the online world is engagement. This means that whichever large company, small business, or individual can engage their audience most effectively, wins.  When it comes to web analytics, it is important to identify the metrics that signal strong user engagement and to work to improve upon the mover time. Some of these metrics include watch time, average view duration, re-watches, returning viewers, etc. The better you can get these metrics, the more engaged your audience will be.” - Jeremy Lawlor, Co-Founder & Chief Strategist at Active Business Growth AI-Enabled Data Interpretation  “AI is constantly being mentioned as an enhanced way to interpret and help visualize data, and we are seeing various tools that promise to do that. I also see a lot of potential in using Web Analytics to predict consumer behavior and have better forecasts of performance. In general, we will have better ways to decipher them in order to better serve our needs in understanding the historical data and identifying trends and risks.”  - Gabriel Shaoolian, Founder and Executive Director at DesignRush A Three-Pronged Shift of Integration and Visualization Data connection web + mobile + offline (data integration)  DataViz development (data visualization) Development of the data-driven business model  "This shift towards a more integrated and data-driven approach should be important to business. After all, it’s the business model not just of the future, but now." - Krzysztof Surowiecki, Managing Partner at Hexe Data, a Data Analytics company A Growth Spurt   “Web Analytics should expect a growth spurt in the next decade. Technology has gotten so complicated, we need people – senior level people – with experience to understand and distill data to the business. Even something as simple as Salesforce, needs experts. Jobs aren’t going anywhere.  Businesses are desperate for educated individuals to help them make sense of all the data and filter out that which “fake” (read: bot traffic) in order to really understand the numbers and what that means for their business.”  - Daniel Levine, trends expert and keynote speaker at DanielLevine.  Web Analytics has been backstage long enough. As businesses aim to strike a balance between the data measurement of the customer journey, ensuring customers’ data privacy, and tailoring each experience, there’s plenty of work here to go around. And with the estimated growth spurt in the next decade, now’s the time to jump in. If you’re interested in Web Analytics, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com. 

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