I recently passed my 100-day mark as Lazada’s Data Science Lead. Through this period, it wasn’t always clear what to do, or how to do it, in my new leadership role. I had numerous questions about how to transition from an individual contributor to leader, how to lead former peers, etc.
Several mentors, books, and articles provided guidance on how to transition successfully. Looking back on these first 100 days, here’s some things I did that were helpful.
As an individual contributor, I had the opportunity, and was expected, to know my project inside out. I was deeply involved in the technicalities, writing code, measuring impact, and gained immense technical satisfaction from this depth. In contrast, as a leader, I was expected to know all of the team’s projects in significant, though not necessarily complete detail, and get involved when necessary. I had to learn how to switch contexts quickly, and be comfortable with not knowing all the nitty gritty details.
In addition, my new role meant my peers now reported to me. I was aware of the burdens of leadership, such as no longer being able to share information that I previously could as a peer. Mentors also warned that former peers might be less chummy with me, due to the new reporting relationship. Thus, I had to change my thinking on my relationships with the team—we might not remain as close as before and this is a natural given the new leadership role. One mentor suggested that I connect more with peers at my new level to get advice and build more relationships.
As an individual contributor, I was clear how my work contributed to Lazada’s mission and added value for our customers, sellers, and the company. As the team lead, I had the additional responsibility of aligning how the team—as a whole—would contribute to the company’s mission.
To achieve this, I met with my leaders, the Head of Data (John) and the CIO (Klemen), to understand Data Science’s mission. I also met with key stakeholder leaders to understand their priorities and how Data Science can contribute to their success. These helped build mutual understanding and trust, and enabled me to build a data science roadmap that was aligned with the company’s mission and delivers results.
One habit I started was monthly (optional) 1-on-1s with the team. This provided an opportunity to learn about each team member better in a private and casual setting. Some members are pretty shy and tend not to proactively reach out—the 1-on-1s ensured they would have up to 60 minutes monthly to share any concerns, opinions, suggestions, etc.
I’ve come to really enjoy the 1-on-1s, where we give each other feedback on how to improve, and discuss about their career plans—this helps greatly with development. I found it helpful to always have a couple of questions prepared for 1-on-1s to solicit feedback on how the team can improve. For example:
Being a builder at heart, I enjoy getting involved in the technical details and writing code—however, this is not scalable. To accomplish more as a leader, I had to learn how to empower the team and delegate. I consulted mentors extensively on how to do this better and the book “The Art of Action” was very useful as well.
Initially, I expected some loss of technical satisfaction when transitioning from an individual contributor to a leader—this was not the case. I could continue to work on POCs (proof-of-concept) and projects—though to a lesser extent—and gained breadth across all team projects. In addition, I found tremendous satisfaction in helping team members grow, and giving them ownership, support, and challenging opportunities to develop their careers.
Through the conversations I had with my leaders, team, and stakeholders, I gained a sense of the values and culture we should encourage to ensure team success, growth, and well-being. In addition, I researched the culture at companies I admired (e.g., Netflix for being able to pivot quickly from mail-order DVDs to streaming entertainment, Valve for moving from games to platform, Google for managed chaotic innovation, etc).
From this, I created a living culture deck to communicate our team culture, values, and other essentials (e.g., encouraging innovation and risk, independence and responsibility, hiring philosophy, etc) and had focus groups with the team to incorporate feedback.
The culture deck’s not quite ready for sharing with the world yet—it needs a bit more work. The process of creating, and the culture deck itself, will be the subject of the next article.
Update: Post on the culture deck here.
Join 4,000+ readers getting updates on data science, data/ML systems, and career.
Welcome gift: 5-day email course on How to be an Effective Data Scientist 🚀