According to Jake Voytko, data science and engineering teams run more efficiently and spread knowledge more quickly when there is a single person setting the technical direction of a team. The so-called “tech lead“.
Sometimes tech lead is an official title, referring to the position between an engineering manager and the engineering team. Oftentimes it is just a unofficial role one grows in to.
Now, according to Jake, you can learn to become a tech lead. And you can be good at it too. Somebody has to do it, so it might as well be you! It could allow you to leverage your time to move the organization forward, and enables you to influence data science or engineering throughout the entire team!
In this original blog, which I thoroughly enjoyed reading, Jake explains in more detail what it takes to be(come) a good tech lead. Here just the headers copied, but if you’re interested, take a look at the full article:
Less time writing code
Helping others often (esp. juniors)
Helping others first
Doing unsexy, unthankful work to enable the team
Being an ally (of underrepresented groups)
Spreading knowledge, or making sure it spreads
And this is what Jake feels his work week looks like as a tech lead:
I wrote about Emily Robinson and her A/B testing activities at Etsy before, but now she’s back with a great new blog full of practical advice: Emily provides 12 guidelines for A/B testing that help to setup effective experiments and mitigate data-driven but erroneous conclusions:
Like any large tech company, Etsy relies heavily on statistics to improve their way of doing business. In their case, data from real-life experiments provide the business intelligence that allow effective decision-making. For instance, they experiment with the layout of their buttons, with the text shown near products, or with the suggestions made after a search query. To detect whether such changes have (ever so) small effects on Etsy’s KPI’s (e.g., conversion), data scientists such as Emily rely on traditional A/B testing.
In a 40-minute presentation, Emily explains how statistical issues such as skewed distributions, outliers, and power are dealt with at Etsy, among others using bootstrapping and simulations. Moreover, 30 minutes in Emily shares her lessons when it comes to working with (less stats-savvy) business stakeholders. For instance, how to help identify and transform business questions into data questions back into business solutions, or how to deal with the desire to peek at the results of experiments early.
Overall, I can the presentation below, the slides of which you find on Emily’s GitHub.