Steps for developing top skills in new data science hires
(Editor's note: This is part two of a two-part series. Part one can be viewed here).
In Part 1, I discussed how the famed data science Venn diagram can be conceptualized as the blending of “Geek” (programming), “Nerd” (statistics/modeling) and “Suit” (domain expertise/business acumen) skills, with the elusive “unicorn” at the center representing a hybrid of all three.
Here at Elicit, we’ve used this framework to define specific competencies within Geek, Nerd, and Suit, and explicit criteria to assess a new hire’s level of mastery within each competency.
There are four levels—“entry level,” “intermediate,” “advanced” and “senior”—corresponding to four job titles: Data Scientist I, II, III, and Senior Data Scientist. When we hire a data scientist, we assign them one of these four job titles.
In this article, we’ll discuss what we do next: how we begin to develop our new hire across the Geek, Nerd, and Suit skill sets.
The initial job title sets the expectation for both the individual team member and the broader Elicit team. For example, a Data Scientist I or II is encouraged to look to a Senior Data Scientist for help with algorithms and programming. But they can also look to a manager or a member of our Suit team for help with effectively communicating a business problem or solution.
Similarly, we expect that a Senior Data Scientist or Data Scientist III can be relied on to provide leadership to other team members across Geek, Nerd, and Suit functions.
With that in mind, let’s onboard our new data scientist through the Geek Nerd Suit framework.
We need to ensure our new hire has access to the all of the technology they require to perform their job, paying particular attention to the kinds of assistance they require in getting this set up.
Whether you’re a Python, R, or hybrid organization, your data scientist is going to require a degree of help getting hooked into your systems and learning best practices for data wrangling, coding, validation, and reproducibility. You’re going to want to provide them with an applicable mentor who can help them through this phase. That mentor is also going to help you evaluate the new hire on their Geek skill set.
This is generally why a data scientist is hired—they can do cool machine learning stuff. But you need to be able to evaluate that, and we leverage the specific competencies and levels of mastery we’ve defined for the Nerd skill set.
For example, we assess new hires on their ability to understand and articulate the tradeoffs between modeling methodologies, which gives us an idea of the best development path for them individually. In addition, we employ a rigorous quality control process around all of our work, which is a great way for a data scientist to learn, and also gets them up to speed on the expectation of how their code should perform.
Again, a mentor in this area is critical, as it gives the data scientist someone they can confidently go to with questions. In turn, that mentor becomes a great resource for evaluating your new data scientist’s progress and overall fit with the team.
This is the hardest skill to find in a data scientist, which is why it’s our most extensively evaluated and managed area. We leverage a variety of techniques to prepare our data scientist for being great storytellers and communicators.
All data scientists are expected to be able to translate their work into a compelling story that solves our clients’ business problems with a level of care, quality, and attention to detail that reflects the lofty expectations of Elicit’s brand. To help develop this skill, we have created a Delivery Excellence framework consisting of best practices for managing projects, creating client deliverables, and clearly communicating with business stakeholders.
Learning this framework helps our team understand the responsibilities expected of their role and practice them on projects from day one. And members of our Suit team are available to help coach our data scientists on how to best apply the framework on their projects, creating engaging stories and deliverables that will delight our clients.
Data science is an evolving field and it’s important to remember that when hiring and bringing on a new member of the team. Techniques and technology are changing faster than ever, which requires these team members to be set up for success from the start.
For us at Elicit, that means recognizing a data scientist is striving to be an expert across the three fundamental areas of a business: Geek, Nerd, and Suit. In order to evaluate, nurture, and ultimately grow that talent, the experts in those areas of business need to provide guidance and critical feedback that will help data scientists along their journey.
It takes a collaborative community to groom a true data scientist unicorn. Good luck.