Source: Metis | March 25, 2019 Author: Brendan Herger Data Scientists are in high demand, particularly as data changes the way many companies do business.
While the market has cooled down since I finished grad school a few years ago, demand still far outstrips supply, and hiring data scientists can be a Herculean task.
In a previous role, I was fortunate to have led or supervised more than 200 interviews, building a team from 2 to 85 people over the course of a year and a half.
While this hyper-growth will probably be unique in my career (as it should be), I’ve picked up a few things about selecting and supporting data science talent.
Opportunity In a previous post, we looked at how building a data science practice within your company can reduce stagnation risks and drive the bottom line.
However, a big hurdle in that process is attracting (and retaining) data science talent in a job market that is growing at an unprecedented rate.
Well chosen data scientists can be worth their weight in gold, helping to gain value from your existing data, empower blue-skies projects, and increase data literacy within your ranks.
Good data scientists tend to be swiss army knives of software engineering, data engineering, and machine learning, and this versatility can make them valuable assets.
Define role(s) Data science is a dangerously broad and vague term, and this vagueness can be damaging as employers and employees set expectations.
While not universal, I’ve found it useful to use Data Scientist as an umbrella role, with three specializations: Analyst (mostly SQL and data evaluation), Data Engineer (mostly data warehousing and data transformations), and Machine Learning Engineer (mostly data modeling and machine learning).
While even these specializations share overlap, an Analyst (Data Science team) opening is more likely to result in qualified and interested candidates than an overly broad Data Scientist opening.
It’ll also help ease conversations around required skills, compensation, and career growth.
Defining these specializations will also allow your candidates to begin forming an efficient assembly line, rather than tripping over each other with overlapping responsibilities.
Small teams represent one possible exception to this rule, because team members often wear all three specialization hats.
In this case, just be aware that if you’re looking for a data scientist unicorn, you should also be prepared to fight (and pay) for one.
Also, folks who can fill all three specializations tend to be drawn towards the Machine Learning Engineer title.
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