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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. 

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