With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
Max started his career in recruitment in 2014, working with UK based recruitment agencies, before joining Harnham as one of the first San Francisco hires in 2016. In this time he has helped to grow the Data Science team and successfully led Data Science recruitment on the West Coast. Max has also been a regular blog writer for Data Science & Machine Learning hiring trends and has spoken at conferences in the Bay area on winning the war for Data Science talent.
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
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 email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
05. September 2019
Smart phone. Smart home. Business Intelligence. Artificial Intelligence. Each designed to improve our live. These smart devices know our favorite music, our favorite movies, the just right temperature for the house, and when we’re away, we can watch it all from our mobile phone. Elements of the four work together for convenience and peace of mind. But, what about your business? What does the future hold for AI and Big Data when it comes to enterprise? As we round out 2018, and look to the new year, the Santa Clara Convention Center, Silicon Valley will be abuzz for two-days at the end of this month with its AI & Big Data Expo North America. Industry leaders will come together to deliver AI and Big Data for a smarter future. A More Centralized Systems Focus We know Business Intelligence (BI), Customer Intelligence (CI), Artificial Intelligence (AI), and more. But, what about a more overarching turn of phrase for enterprise as a whole? The intelligent enterprise. Imagine advanced analytics, intelligence applications, and machine learning seamlessly connected via the cloud for more flexible decision making based on data flow into business systems. To do this, your business needs a strong data foundation and centralized team from all departments for an at-a-glance dashboard view of your business and its needs. Having the right data coupled with the right computing capabilities offers single view information at each customer touchpoint. Once siloed positions now molded into one unit with the right information can help you make better decisions, faster. AI Application Expectations for 2019 We’ve come a long way from the days of the Jetsons, in which AI is simply a robot programmed to do mundane tasks. Today, and looking toward the future, we’re in a real-time world closer to that of Bicentennial Man. In fact, much like our personal devices are extensions of ourselves, businesses are beginning to see AI as extensions of their business, contributing to their success. AI is continuously evolving, and 2019 will be no different. As companies embrace and work to enhance machine and human relationships, we’ll begin to see and understand AI’s limitations, and when and how human interaction may be needed. Though evolution of AI is expected to triple in the new year, many businesses currently using its applications for mission-critical processes will grow more confidence in new technologies. Businesses once on the sidelines will grow into their roles as digital leaders, advancing their industry, and while 16 percent of a recent survey said they are currently applying automation and AI to one or more mission-critical business processes, more than three times the amount are expect to do so by 2019. We’ve barely scratched the surface with what we know will be a jam-packed agenda November 28 and 29 in Silicon Valley. We’ll be at the Expo and hope to see you there. Stop by, say hello, and if you’re interested in Big Data, analytics, and the future of business, we may have a role for you. If you’re a business seeking to grow your Data team, we’re available to guide you to hire and retain top talent. We specialize in junior and senior roles. Check out 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 email@example.com. For our Mid-West and East Coast Teams, call (212) 796 6070 or send an email to firstname.lastname@example.org.
15. November 2018
Hedy Lamarr. Ada Lovelace. Dorothy Vaughn. These are just a few of the names of early women pioneers. Ada Lovelace, a century before it existed, is now known as the first computer programmer. Hedy Lamarr, along with her partner George Anthiel, developed and patented the technology which would become the digital backbone of what makes cellphones, laptops, tablets, and other wireless operations possible. For some, numbers come naturally. For others, communication comes naturally. In today’s technology trends, the ability to blend the two will become ever more important. But, it won’t just be about blending skill sets. It will also be about blending the human workforce and artificial intelligence. But there are still some misconceptions clouding opportunities. Sometimes, it simply comes down to mindset. And breaking those mental barriers is a start toward filling the gap and creating the workforce of the future. Building Bridges One woman helping do that is Kaisa Kukkonen, also known as the ‘Lady AI’. She’s translating business needs into technical needs and has developed AI Excursions for women to build understanding of AI in different professions. Her goal is to build bridges between generations, and between technical and non-technical people, which she hopes will lead to bridges between humans and AI. Ms Kukkoen says: “When it comes to business executives, Millennials and younger generations, there is a massive difference in how technology is perceived in their daily lives. There is a difference of what is considered normal and approachable and non-threatening,” Women in Technology Are Trending From Girls Code Camps to Women in Data Science conferences, women are starting to get heard in the tech world. Across the globe, women, regardless of profession, can try their hand at Programming, Data Science, Digital Analytics, and more. We’re rethinking our approach to teaching and training technology, working towards overcoming mental barriers. As many women as are in the profession now, there probably that many more who were told at some point they weren’t right and that this isn’t their field. Those who have overcome this adversity, however, have made a huge impact. Dorothy Vaughn, one of history’s hidden figures, made history as one of the earliest female programmers for NASA. When the first super computers came into play, she made it her mission to learn everything about it and became an expert FORTRAN programmer. But, it’s more than teaching or training. Ultimately, there is a need for a shift in attitude from the wider tech community. By broadening candidate search criteria and making clear benefits such as parental leave at the start of the interview process, businesses can open themselves up to a more diverse, and more equal, workforce. The Chocolate Chip Cookie Effect Mistakes can lead to great and delicious things. The chocolate chip cookie was never supposed to happen. The woman who ran the tollhouse wanted to change up her sugar cookie recipe and thought she could make a chocolate sugar cookie by adding chopped chocolate. To her surprise and her hungry customer’s delight, the bits didn’t melt all the way, to ultimately become Tollhouse Chocolate Chips. The same can happen when you let students know it’s okay to make a mistake. Because sometimes what you envision on paper isn’t what you get on the screen. A Finnish/Nordic start up edtech company is teaching high school students to combine art and programming. Using a Java-based programming language called Processing, similar to those used in the design field and game industry, they teach programming. But instead of creating the same thing over and over, students create something new. Combined with another course using Arduinos, the students can take first steps towards building their own robots. If you’re interested in data and analytics, and want to up your game in the field. We may have a role for you. Harnham specializes in both Junior and Senior roles. To learn more, check out our current vacancies or contact one of our recruitment consultants. For our West Coast Team, call (415) 614 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 6070 or send an email to firstname.lastname@example.org.
18. October 2018
Data Science teams are hugely important to almost every growing business today. The ability to predict market trends, create new products and augment and automate processes will be a key differentiator in a changing economy, between businesses that thrive and businesses that struggle. As such, the Data Scientist is the hottest and most sought-after job title in today's job market and competition has never been so fierce. Ultimately, competition is only going to get more and more intense for these talented individuals.So how do you win the war for Data Science talent? Over the course of this talk, we're going to look at talent attraction and retention strategies, how to engage hard to reach talent, and how to create stringent and effective hiring processes that will differentiate your business from the competition.Is there a shortage of talent? The answer to this is both yes and no. Run a quick LinkedIn search for a Data Scientist in the Bay area and you'll see a massive 35,783 returned results and 2,604 live roles, however a 2012 government study stated that undergrad stem degrees would need to increase by 34% in order to meet predicted demand for these skills.However at the same time, only 50% of individuals with a STEM degree are employed in a relevant field - this number is roughly 3x higher than other fields of study.32% of Comp Sci degree holders are not employed in an STEM role say that they are working in unrelated fields due to a lack of relevant job.The reality is that for the most part - many of you in this room today are speaking with the same candidates as one another and are looking for the exact same profile. At the same time, candidates are all applying to the same companies, with the same focus, again, for the same roles. The same companies are offering jobs to the same pool of candidates, who have 4 or 5 job offers in hand at any one time, meaning that 75-80% of job offers aren't being accepted.A vicious cycle This is leading to the cycle that I see in the market, every single day.1) A large proportion of candidates are overlooked or excluded, giving the perception of a lack of opportunity2) Employers are struggling to fill their roles due to losing candidates to competitors, feeding the idea of a lack of available talent.What's the Fix? The first step is looking beyond the standard candidate pool - if I were to ask all of you to think of your ideal data scientist, most would say, Masters or PhD in a quant field. Internship somewhere scrappy during the course of their degree. Publications, Patents and Research work. Few years of experience in a production capacity, Python, R, Strong ML background etc etc.Maybe it's time to look beyond that - Organizations such as Galvanize and Metis have created intensive data science programs that are creating well rounded data scientists that most people will look beyond. Let's change the focus of the degree criteria - some of the best coders that I've seen, have been self-taught and have a genuine passion for developing their skills. So how do we find these people? There will always be a time and place for LinkedIn's searching features, but the candidate pool runs so much wider than that. Luckily, you're all at a conference, so I don't need to pitch you on the importance of networking, but there are so many ways to find this talent. Never underestimate your own people, the best people, know the best people. Before you do anything at all, brief your team on what you're looking for and open the floor to them for their referrals and recommendations.Next, create partnerships. Work closely with schools, bootcamps and research facilities to get you access to data science talent quickly and efficiently. You'll also be able to get a fresh approach to those problems you've been looking at and may just stumble on some exciting solutions.Sponsor a competition. So many Data Scientists that I work with are fiercely competitive and love the idea to showcase their skillset. You'll also get a natural interview process taking place, with the cream rising to the proverbial top. Lastly, keep up to date with the market - follow market moves, funding rounds and news stories to look in to redundancies or news stories that may make an individual more open to a move. Finally actively target companies that utilize similar tools, or work on transferrable problems. By no means am I saying headhunt your competitors staff, but why not look at areas that will utilize similar methodologies or algorithms, where someone will be able to come in and hit the ground running.Engaging with this talent - The power of why I'll never forget the best piece of advice that I had received when it came to recruiting Data Scientists. It came from Vin Vashishta (who is definitely someone you need to be following) - Focus on the "why".Your message needs to stand out, and you need to capture the imagination of your potential hire, especially if you're not a traditional "halo" brand. Candidates in this space are mission driven, the what is nowhere near as important of the why. For example, candidates don't care that you're utilizing computer vision - but they do care that you're using it to monitor and track the breathing patterns of infants in their sleep. It's going to be the why that starts the conversation, the what will come after. Engaging with this talent - Growth & Development Next you need to be upfront about growth and development. Not every organization is going to change the world, not every start-up will achieve unicorn status. That's ok. Not every scientist will become a CDO. Don't make promises that can't be kept and be clear about where and how you see this role and this person evolving.Engaging with this talent - Do away with lengthy processes I follow very closely the work of some exceptional people, and I love seeing Data Science applied to hiring processes. One such person who I follow is Emily Glassberg-Sands at Coursera. She wrote an article about how they had analyzed every area of their hiring processes and assessed where people were dropping out, and fixed those areas.Let me tell you now, you don't need to have 5 screening calls, a take home test and an 8-hour interview day. In fact, simplifying this even further, you don't need a take home test. Nowadays most people have a portfolio of code that they'll be all too happy to share, so that you can see first-hand the work that they've done. If you want to understand how candidates approach and tackle a problem, run a whiteboard session, or a webex, where a candidate can feel like they're already working with you, tackling a problem in unison.In any hiring process, you're getting interviewed as much as you're interviewing. Your process reflects who you are as a business, long and drawn out - means slow and clunky. There's an organization whom I know of, who are doing a huge hiring drive, with an average turnaround time of 8-10 weeks per hire. As a result, candidates are getting half way through a process, getting messaged by another organization and are off the market a week later.Focus on what is a necessity, a stage focusing on technical capability, a stage focusing on role suitability and a stage focusing on cultural fit. All in all, three stages should be more than enough, and should take no longer than 10 days. Set expectations clearly at the beginning. Hiring is a time-consuming process and losing time interviewing a candidate whose expectations are not aligned with your own, is a waste of time, purely and simply. Have the difficult conversations as early as possible will save you time further down the line. Put bluntly, if/when you work with a recruiter, the vast majority of people ask us to find a candidate's salary expectations, however most organizations when they recruit for themselves, they do not have that conversation until the very end of a process.Engaging with this talent - Closing candidates The most effective and efficient processes mean nothing if ultimately you can't get a candidate across the line. This is where your recruiter - either internal or external is going to really earn their money. Understanding the push and pull factors in a decision is key to a successful hire. A high base salary means nothing if that isn't the reason that a candidate is looking for a move.The key here is that when you've identified your hire, strip everything back to the bare bones. Go through the role again, the company again, understand any concerns that they may have and set up conversations with decision makers again if necessary. Close candidates on numbers at which they feel happy, but that also mean value for money for you - DON'T LOWBALL!!! I see all too often companies that can go higher, go in with a lower offer to try and get a discount on a hire, every now and again, you'll get a positive resolution from this, but more often than not, you'll scare off a candidate who has the potential to feel undervalued, and therefore warn other people in their network about a negative experience. Make offers that are fair, for both parties and explain how you got to those numbers.The closing process is the most in the whole recruitment cycle. A botched close will mean that you start back at square one and that all of the work that you've done starts back at zero. Retaining this talent - Prepare for churn Unfortunately there is no secret sauce here. It's going to happen. By the very nature of hiring scientists, you're looking for people who are naturally inquisitive with a thirst and passion for learning and development. More often than not, you'll form part of their development, as opposed to all of it.The average Data Scientist is currently switching role roughly every 2 years. Your job is to lift that number as high above the average as you can. The key here is to understand who your Scientists are as people, what's important to them in their future, and help to meet as many of their goals as you can.Retaining this talent - Invest in your people The old saying rings true - "what if we invest in our people and they leave - but what if we don't and they stay". You need to give your scientists the freedom and the platform to be the vest version of themselves that they can be.Lastly, don't wait too late to reward your top performers. A counter offer is always an offer too late. If someone is performing well, let them know that they're appreciated with that promotion or raise that they deserve. Don't wait for them to come to you with their 2 weeks notice. If you wait that long, you're too late.Retaining this talent - People leave bosses I don't believe this to be a fundamental truth. As I mentioned earlier, I firmly believe that the why is the most important thing in developing great scientists. As long as your mission is one that excites your people and that you're constantly following your north star, getting closer and closer. Your scientists will be driven by that same mission.Ultimately your role as Managers, Directors, VP's and Execs is to nurture the talent within your ranks, create an environment where your people can thrive, and where they know that they'll continue to do so.
25. January 2018
The data science sector can provide a myriad of career opportunities for skilled, savvy professionals with an interest in data and analytics. This profession can often be determined by technology and trends, making for a dynamic, rapidly-developing industry that is growing at an unprecedented rate. We don’t see the market slowing down for data science jobs any time soon, and the following predictions on the state of the industry should only further stimulate interest in this field. Here’s what’s expected for data science in the near future: Artificial intelligence takes hold Artificial Intelligence (AI) has been a buzzword in many technical and non-technical industries for the past few years, and it’s making waves in the data science industry due to its ability to improve efficiency and provide new insights from existing sets of data. Data science professionals who have struggled with the idea of completing monotonous repetitive tasks will welcome the introduction of AI to automate these processes using simple algorithms. High-volume repetitive tasks can be transformed thanks to AI’s ability to do things like clustering, which can help to predict consumer behaviour and tailor suggestions to other consumers accordingly. Reasoning, learning, planning and natural language processing can all be employed by AI to do everything from quickly learning more about large groups of data through to building and sending automated messages. As most AI processes require a significant volume of data to train and hone, the use of such technology within data science will continue to evolve as the industry does. Security gets stronger Every year sees significant data breaches across major international firms, and 2016 was no exception. According to the Data Breach QuickView report, 4,149 data breaches exposed more than 4.3 billion records last year. No industry is immune to data loss, but such breaches can not only lead to financial loss, but also, reputational damage and security issues. And while some attacks are unavoidable, the Online Trust Alliance reports that more than 90% of cyber incidents could have been prevented. This suggests data security will continue to step up over the coming years, with response and recovery becoming an increasingly important component of any data security programme. With many organisations hesitant to invest heavily in cloud computing due to perceived issues surrounding data security, awareness will need to be increased on the safety of cloud computing and the treatment of user data. Identity management, access control, data leakage and virtual machine protection will continue to be security issues organisations will need to assure consumers of in order to build trust and confidence. Machine learning shapes up Machine learning has traditionally been used in complex problems that would otherwise be handled by humans. Machine learning is when AI acquires self-learning capabilities, using example and experience data to predict or describe. Machine learning has been time-consuming and expensive in the past, however with new applications leveraging Big Data, new versions of machine learning will work more effectively to understand the context of newly streamed data, therefore ‘learning’ more quickly. As more organisations make better use of Big Data and investigation into machine learning continues, expect to see static and dynamic data used to improve performance. AI and machine learning will combine to deliver systems that not only understand, but also learn, predict and adapt, potentially even working autonomously. Are you interested in data science? The above trends are just a few of the developments we can expect to see in Big Data over the coming years. As the industry continues to advance and demand for skilled professionals grows, there will be plenty of opportunity for you to make your mark. Take a look at our latest data science jobs here. For the East Coast and Mid-West teams please call 212-796-6070, or email email@example.com. For the West Coast team call 415-614-4999 or email firstname.lastname@example.org.
06. September 2017
Predictive analytics in healthcare has been one of the fastest growing trends in recent years and it is scaling up over the next three. With this industry top of mind in the news and those closest to it; patients, providers, and insurance companies realizing the penultimate benefits of relative data, healthcare analytics is a solid foundation for anyone interested in the field of analytics. Beyond collection and analysis is the deeper dive of meaningful use in regard to artificial intelligence (AI) and machine learning within datasets and analysis.Smart Health and WellbeingThough big data in healthcare analytics has previously lagged behind other industries, it now faces a massive upheaval. Over the last ten years or so, simply moving patient’s records to electronic health records (EHR) and storing them in the cloud for at-your-fingertip information, is the tip of the iceberg.Big data in healthcare has grown up. Researchers and providers are able to make more informed decisions, more quickly gleaning information from such records and using the data to predict diseases, make better diagnoses, and determine patterns. Some of these patterns are especially useful in patient empowerment and support for such chronic diseases as Alzheimer’s, cancer, diabetes, and Parkinson’s.As complex as the healthcare system is, few healthcare organizations have the resources or skilled personnel to develop and analyze such intricate data. There is a shift, however, in the application and access of big data healthcare analytics and that is an entry in the as-a-service industry.Healthcare Tech in the CloudThe advent of cloud computing has made it possible for organizations with limited resources – skilled personnel and in-house knowhow – to progress their health management programs. Cloud-based tools and applications allow organizations to reduce infrastructure and development frustrations to focus on value-based care.The machine learning as a service market is skyrocketing and at a growth rate of 38.40 percent annually is expected to be worth $20 billion in the next 20 years or so. This sets machine learning as a service industry in healthcare on a trajectory of $5.4 billion by 2022, according to a 2016 report.Machine Learning as a Service combine with artificial intelligence could bring over $46 billion to MLaas vendors could revolutionize healthcare by 2020. Driven by big data analytics, healthcare organizations may find cloud-based tools and machine learning applications easier on the budget with a broad range of proactive measure capabilities in the industry.This year, the driving force behind most of the artificial intelligence spending is in quality management, diagnosis and treatment systems, customer service, and security of data. According to a survey conducted by Silicon Valley Bank last year, healthcare AI is expected to the most influential technology in 2017.In a rapidly changing environment, both technologically and organically, the analytics required to succeed in the healthcare industry may lie in machine learning as a service. This as-a-service focus may also be a cost effective way to engage the large, complex amounts of data derived from the players in the healthcare industry.If you’re interested in contributing to the future of big data, we might have the role for you. We specialize in Data and Analytics recruitment and always have a wide range of vacancies at both junior and senior level. Take a look at our current vacancies or contact us to find out more.For the East Coast and Mid-West teams please call 212-796-6070, or email email@example.com.For the West Coast team call 415-614-4999 or email firstname.lastname@example.org.
01. July 2017