Like other roles, data scientists need to have a few fundamental needs: Such as: ownership of, and credit for their work, ongoing education, and access to mentorship. Here are a few ways to achieve these goals:
- Make it easy to deploy AI models quickly
- Be sure data is stored in such a way that it facilitates team throughput/efficiency
- Increase number of group projects/involvement
- Hold frequent individual reviews
Here are each four in more detail:
Reduce Deployment Time
When data scientists struggle to deploy their models, their work and hopes go unfulfilled. It’s essential to facilitate quick model deployment. This starts with a good relationship with dev ops. Be sure dev ops understands exactly what they’re deploying and how often it needs to be refreshed. Develop an understanding of boundaries between dev ops and data science so dev ops isn’t trying to recode models and data science isn’t trying to provision cloud resources or tune load balancers. When this is done right, data scientists feel empowered to build more and better models. With long deployment time, data scientists start to wonder how their work helps the company, and sooner than later, they’ll go off to another company where they can deploy their work and get credit for it!
Increase Team Throughput
Storing data in the right format can drastically reduce the time a data scientist spends handling data. This means more/better models get built (and deployed) sooner. Imagine the case where a simple task like pulling some user data takes hours of complex joins across 10+ tables. That’s a recipe for a lot more than just errors. It slows everyone down. Fixing this problem by creating a user aggregate table (for example) fixing the problem so that all subsequent pulls can be done quickly. Find out what data your team uses or needs daily or weekly and make sure they have straightforward access to it. I’ve seen this technique unlock teams, with the added bonus of all-important data consistency.
Work in Groups
Group engagements are more efficient, more satisfying to the team and achieve better results faster. If you have projects with a single technical resource, consider adding another person. I’ve found it to be only beneficial.
Have Frequent Reviews
This can be quite time-intensive for management. In particular, it requires real time commitments as well as an agreement on where each data scientist stands in the company. Data scientists are like any other employee in that they want to know where they sit on the career ladder and what’s required to advance to that “next level”. Going over major accomplishments is easy and is something most leaders do at least weekly with teams. Guidance on upcoming work is more challenging and positioning can be quite delicate. This one can be lots of work but is well worth it in terms of keeping employees who might otherwise move away.
Periodic (quarterly) one-on-one reviews work well. Topics covered:
- Major accomplishments
- Upcoming projects/goals
- Kudos for good work
- Guidance for upcoming work
- Position in company/team
I’ve used these four techniques extensively and have seen very positive results. Hit reply if you’d like to let me know if you think I missed something fundamental. I’d love to hear from you!
Of Interest
Data Science is not a Science Project
Wow, what a title! That’s exactly how I feel. Data science is there to support products, not be an excuse for a room full of smart people to science the heck out of a mountain of data. I know how strange this sounds, but it happens all the time and costs tens of millions of dollars each year in wasted productivity. The report says that at least half of analytics results never make it into production. Wow, we can do better! Here’s the article: https://www.technologyreview.com/s/614431/data-science-is-not-a-science-project/
Worth Reading Again and Again
Google published this Rules of ML many years ago and it’s still my favorite read on the topic. I highly recommend it! https://developers.google.com/machine-learning/guides/rules-of-ml
Watch a robot solve a Rubik’s Cube
I never learned to solve these when I was a kid, but they always fascinated me. This post is well done, with interactive visualizations and fun videos.
https://openai.com/blog/solving-rubiks-cube/
The Yes
30 data scientists join the former COO of StitchFix. They’ve raised a ton of capital. This is one to watch! https://www.voguebusiness.com/technology/julie-bornstein-the-yes-funding-ai-powered-shopping-platform