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January 27, 2017
How to Recruit Data Scientists
Data science is hot. Data scientists are even hotter, since demand far outstrips supply. The race is on to find skilled people who can organize, structure, and make business sense out of big data sets. People with heavy math, analytic, and conceptual skills, and the attendant personality traits, can virtually write their own tickets. Poaching data scientists has become a blood sport.
But recruiting and retaining top-flight data people requires an understanding of their personalities and motivations above and beyond escalating salaries, free lunches, and other perks. And while there are many individual personality variations, several factors should shape your engagement strategy.
Brand Affinity. Brand awareness and reputation cuts both ways among the data science crowd. Many want to work at Google, Amazon, and other giants known for pioneering work in artificial intelligence (AI) and machine learning. This is especially true for younger players who want to validate their bona fides and get a marquee credential as a career springboard.
In other cases, data people warm to the prospect of working in scrappy data-heavy start-ups, where equity and fame are options. Others see career value in playing a bigger role in a smaller environment earlier in their career or in honing skills and gaining experience in a data-centric software or consulting firm.
Peers. Data guys want to be constantly learning from peers and finding potential mentors. Birds of a data feather flock together. The number, experience, and pedigree of peers have to be part of any recruitment story. Great data science teams are persistent learning labs where ideas, techniques, and approaches are shared, debated, tested, and refined.
Present your data science bench and position a new recruit in the environment in ways they can visualize or anticipate their role. Management must be serious about data science and not only talk the talk. Managers with data chops need to be front and center with a keen understanding of the math and the psychology of being a data scientist. 
Tout Toolsets. Data scientists want to be on the cutting edge using and maybe even inventing new tools. Be sure your IT landscape, servers, and infrastructure are near the latest generation with maximum capacity. If you’re simply doing serial regressions on five-year-old machines or not working in Hadoop don’t bother seeking out these guys.
Understand that the availability and expert level users of R, Python, deep neural networks, trainable models, and other of-the-moment software and math techniques are data science crack. Everyone is forward thinking. Presenting an innovative and agile workbench is critical for people who are religious about adding skills to their personal toolbox.
Client Challenges. Everybody wants to work on something that matters.  Data people have a Trekkie-like focus in going where no one has gone before. Working on big challenges for well-known clients is a huge turn on. Solving complex business problems by finding something new or different in the data is extremely gratifying and highly motivating.
Effectively seducing data scientists requires exposure of client names and issues as well as dangling opportunities to advance the client’s and the scientist’s cause. A corollary is explaining context; why the work matters, how it fits into a client’s business model, and how the work might advance the field of data science. Nobody wants to be the unloved geek in the back room crunching numbers.
Workflow. In most cases, there is an enormous and time-consuming amount of data ingestion, preparation and normalization necessary when working with big data sets before anyone can make data magic. Data guys want to know the ratio of dull or routine work to interesting predictive analysis they can expect to do.
Some of this turns on the available toolset, some on internal hierarchy, and some on management tactics. Recruiting top talent requires an attractive ratio of routine to breakthrough work assignments and a modest amount of self-directed work or choice of assignments.
Finding and landing top data people demands an understanding of their needs and a competitive commitment to super-serve them. Managers and companies must engage data recruits on their own terms and paint an enticing picture of the brand, the environment, the challenges, and what they can expect to do and to achieve.

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Danny Flamberg, EVP Managing Director of Digital Strategy and CRM at Publicis based in New York, has been building brands and building businesses for more than 30 years.Prior to joining Publicis, he led a successful global consulting group called Booster Rocket, as Managing Partner. Before becoming a consultant, he was Vice President of Global Marketing at SAP, SVP and Managing Director at Digitas in New York and Europe and President of Relationship Marketing at Amiratti Puris Lintas and Lowe Worldwide.
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