Onboarding as a Data Scientist
You got the job! now what?!
Very recently, I started as the new Product Lead of a multi-million dollar e-commerce company based in LA, California. I’m responsible in participating and providing product guidance over data science and analytics innovation
initiatives in the company — to create and define new processes for the improvement of business decisions and opportunities.
While my new roles leans toward data leadership, our team is very much a startup team — the only difference is we’re handling millions of records every week. So, the manpower of a startup team, but the data operations at a scale of a large corporate company. That means, I’m hands-on as well, with all the technical and business aspects of our data products.
Joining a storied company with over a decade of history means that I have a lot to learn in a short amount of time. As part of the onboarding process, I have created a 30–60–90 Day Plan to outline my transition to this new role. Also included in this blog are questions I used in my Listening Tour.
Here’s my condensed version of the Cold Start Algorithm + Listening Tour Questions (courtesy to Gokul Rajaram)
- tell me everything you think I should know
What is working well?
What is not working?
What should we do that we aren’t doing today?
What would you do if you were in my shoes?
What should I learn about Posh Peanut that will be helpful to me in my role?
2. biggest challenges the team has right now
3. who else should I talk to
From the notes I took doing the Cold Start Algo, I created an early State of the Union.
To retrofit Deb Liu’s notes as CEO to onboarding as a Data Product Lead, here’s who I met with:
- Every member of the data team
- Nominees from anyone I met with
- People in interesting roles who had a unique perspective I encountered throughout the month
- My manager
- Other coworkers on my team with whom I’ll work closely
- Other colleagues who are in my role or a similar one
- Any cross-functional partners (on other teams) I’ll work with regularly
- Any external partners (outside of the company) I’ll work with regularly
- My new direct reports (given I’m a manager)
Communicating the 30–60–90 Day plan as a Product Lead (Data & Analytics) means a balance of product guidance in parallel with early execution (in the true agile sense). This means that while I was setting up the data product roadmap for the company, I was also, at the same time, executing data sprints aligned with that roadmap. This doesn’t necessarily mean I’m following scrum religiously … it’s dangerous to blindly follow the frameworks (scrum, agile, SaFe) without truly understanding what fits the needs of your organization.
In our team, TDSP makes the most sense as we want a data team that will work and deliver quality products in the long run — and not burn them out in an endless cycle of consecutive short sprints with no breaks.
As a note, I joined as a wartime Product Lead. That means I had to adapt these themes into something that got me into the action faster.
Goals
Setting goals is all about making a plan for how you’ll achieve your overarching priorities. For each phase, set goals that ladder up to your stated focus and priorities. (See our example 30–60–90 day plan below for inspiration.) If it’s helpful, break your goals into categories like learning, performance, and personal goals.
Learning goals: To set these, ask, “What knowledge and skills do I need to be successful? How can I best absorb and acquire that information and those abilities?”
Performance goals: These are concrete things you want to accomplish or complete as part of your new role. To set these, ask yourself, “What progress do I hope to make within the first 30/60/90 days?”
Personal goals: These goals are more about getting to know the people you’ll be working with and finding your place within your new company or team. To set these, ask, “Who are the key people I need and want to build relationships with? How can I establish and foster those relationships, so that I’m seen as trustworthy and credible?”
I presented my 30–60–90 day plan to the team and to my manager, got their feedback, then iterated based on their feedback. I also presented some of the data MVPs I worked on along with my 30–60–90 day plan so that they know that my proposals are feasible and something I can work on. The data MVPs I built also helped me get feedback on whether there’s a different feature I need to prioritize or if some of the features are non-priority (i.e. should be eliminated from the first sprint).
You can definitely retrofit my approach when onboarding onto other roles — e.g. as a Data Scientist. Just be mindful of the company culture and how the business works, then you should be able to do a cold start algorithm, starting Day 1!
PS if you are an undergrad Filipina from a low-income family and you’re trying to land your first job in tech / data, please reach out to me, and I’ll help you #GetThatBread 😉
If you’re interested in finding out more about the data science interview process, here’s my previous blog on ACTUAL data science interview questions.
