Latest / The Career Mentor Podcast with Fexingo: Finding Mentors, Sponsors, and Career Advisors / How to Find a Mentor When You Are a Data Scientist
Transcript
- Lucas: So Luna, I've been thinking about a conversation I had with a data scientist at a mid-sized fintech company a few weeks back. She's brilliant with models, but she told me she feels stuck because every time she tries to move from building the analysis to shaping the product roadmap, she hits a wall. And her first instinct was, I need a mentor. Luna: Yeah, and I bet she's not alone. Data scientists often have this weird dual problem: they're in high demand, but their career paths are murky. A mentor could really help parse that. Lucas: Exactly. And that's what we're digging into today. Finding a mentor when you're a data scientist is different from other tech roles. It's not just about learning the ropes of the codebase; it's about translating technical work into business impact, and that requires a different kind of guidance. Luna: So where do you even start? I mean, data scientists are often surrounded by other data scientists, but the people who can actually help them grow might be in product, or marketing, or even sales. Lucas: Right. The classic mistake is to look for a mentor who looks like you — another data scientist, a few levels up. But that might just reinforce the same blind spots. For this woman, the more valuable mentor could be a product manager who's used data to drive decisions, or a director of analytics who's made the leap into leadership. Luna: That makes sense. It's almost like you need a mentor for your technical craft and a separate mentor for your career trajectory. Lucas: Precisely. And that's a core point. One mentor rarely covers both. So today, we're going to talk about how to find that career-focused mentor, specifically when you're a data scientist. We'll look at why the usual playbook often fails, what to ask instead, and a concrete approach to landing the right person. Luna: I'm curious about the 'why it fails' part. Because on the surface, it seems like there are tons of people willing to mentor in tech. Lucas: There are, but the mismatch happens on a few levels. First, data science is still a relatively young discipline, so there aren't as many senior data scientists who have already navigated the path you want. Second, the problems data scientists face are often more ambiguous — they involve stakeholders, data quality, and organizational politics. A generic tech mentor might not have the context. Luna: So what's the alternative? Look outside your immediate function? Lucas: Often, yes. For example, think about a data scientist who wants to move into a product analytics role. A mentor who is a senior product manager, or even a director of product, can give you insight into how data shapes product strategy, how to communicate insights to non-technical teams, and how to influence decisions. That's gold. Luna: But how do you even approach someone like that? Cold emailing a VP of Product might feel intimidating. Lucas: Sure, and that's where the approach matters. Instead of asking directly for mentorship, you might start with an informational interview. You can say something like, 'I'm a data scientist on the payments team, and I've been really impressed by how you've used data to shape the checkout flow. I'd love to hear more about your career path and how you think about that.' That's a low-commitment ask, and it opens a door. Luna: And once you're in that conversation, what's the move? How do you turn it into a longer-term relationship without being pushy? Lucas: That's the key. You listen more than you talk. You ask thoughtful questions about their journey, the decisions they've made, the challenges they've faced. And you look for natural points of connection — maybe they also made a switch from a technical role, or they've worked with data scientists before. Then, after the conversation, you follow up with a thank-you note and maybe a specific insight you took away. That sets the stage for future touchpoints. Luna: So it's about building a relationship slowly, not just 'hey, be my mentor.' Lucas: Exactly. And that's a lesson that applies beyond data science. In fact, I think there's a broader principle here about paying it forward in your career. You know, when someone takes the time to share their experience with you, it often makes you want to help others in the same way. And that's something we see in our own community too. Luna: Yeah, I've noticed that. A lot of our listeners reach out and say how they've been helped by a mentor, and now they want to do the same. It's kind of a virtuous cycle. Lucas: Right. And that's exactly why listener support matters to us. Not because we're counting on it, but because the show is ad-free and independent, and that only works if people who find it valuable chip in when they can. If this podcast has helped you think through a career decision, or even just given you a new idea, and you'd like to keep it going, you can support the show at buy me a coffee dot com slash fexingo. It's a simple way to keep the conversation going. Luna: And every little bit genuinely helps. It's like the mentorship principle — small actions can have a big impact. Lucas: So back to that data scientist. Once she's made that initial connection, what's next? She needs to think about the actual questions she wants to ask. It's not enough to just 'pick the person's brain.' She needs a strategy. Luna: What kind of strategy? Give us some concrete examples. Lucas: Well, for someone moving from pure analysis to product influence, I'd recommend asking things like: 'How do you decide which metrics actually matter for a product launch?' or 'How have you handled situations where the data didn't support the direction a stakeholder wanted to go?' These are the kinds of questions that expose the real-world challenges and show you're thinking about impact, not just code. Luna: That's smart. It's almost like you're interviewing them for the role you want. Lucas: Exactly. And you're also giving them a sense that you're serious about growing. But there's another angle, too — looking outside your company entirely. Data scientists have a huge advantage: the community is active online. Conferences, webinars, GitHub, and even LinkedIn can be great places to identify potential mentors. Luna: But online outreach feels even more awkward sometimes. How do you break the ice with someone you've never met in person? Lucas: It's tricky, but it can be done. Start by engaging with their content — share thoughtful comments on their posts, ask a relevant question. Then, when you do reach out, reference something specific they said. For example, if they gave a talk on model interpretability, you could say, 'I really enjoyed your talk at such and such conference. I'm working on a similar problem and was wondering if you'd be open to a quick chat.' That's a lot more compelling than a generic 'I admire your work' message. Luna: I like that. It's respectful of their time and shows initiative. Lucas: And once you get that chat, treat it with the same care as an in-person meeting. Come prepared with your goals, but also be flexible. The best mentorship often evolves organically, not from a rigid agenda. Luna: So we've talked about finding mentors outside your function and outside your company. What about within your company? Are there specific roles or departments that are under-tapped for data science mentorship? Lucas: Absolutely. One that often gets overlooked is the business operations team, or the strategy team. These are people who are used to working with data, but in a more business-focused way. They can teach you how to frame your analysis in terms of ROI, customer acquisition costs, or other business levers. That's a skill that's hard to pick up on your own. Luna: And they might also have connections to senior leaders, which could open doors down the line. Lucas: Exactly. A mentor like that can be a gateway to sponsorship. And that's important because mentorship is about advice, but sponsorship is about advocacy — someone who will vouch for you when opportunities come up. For a data scientist aiming for a leadership role, you eventually need both. Luna: So, to recast: when you're looking for a mentor as a data scientist, you need to expand your search beyond your own department, be strategic about the questions you ask, and think about the long game — turning an initial conversation into a relationship that could lead to sponsorship. That feels like a solid playbook. Lucas: It is. And I think there's one more piece that's often forgotten: the idea of being a good mentee, which we've touched on in previous episodes, but it's worth repeating. You have to be proactive, follow through on any action items, and show gratitude. That's what makes a mentor want to invest in you. Luna: And if you're a data scientist who's been on the other side — maybe you've already made that leap — what's your advice for giving back? I imagine it's a two-way street. Lucas: That's a great point, and it's something we can all think about. If you're in a position to help someone younger in their career, consider being that mentor you wish you had. It doesn't have to be a huge time commitment — even a few conversations a year can make a difference. And as we said earlier, that's a principle we try to live by here. Luna: Well said. So for anyone listening who's a data scientist feeling stuck, the message is: widen your net, be intentional, and don't be afraid to reach out. Lucas: And we'd love to hear how it goes. If you have a success story or a challenge, you can always reach out to us. But for now, I think we've given you a solid starting point. Thanks for being with us.