By Saeed Mirshekari
December 31, 2024
The Top 10 Mistakes in 1-on-1 Mentorship for a Data Science Career
Mentorship is one of those things that can make or break your career—especially in data science, where the landscape is vast and sometimes overwhelming. When done right, it’s transformative. But when mistakes happen, it’s easy to feel stuck or frustrated. Let’s talk about some of the biggest mistakes that mentors and mentees often make in 1-on-1 mentorship for data science careers—and how to avoid them.
Not Setting Clear Goals and Expectations
This one’s a classic. Picture this: a mentee shows up looking for advice on getting their first job, but the mentor starts diving into advanced machine learning theory. Sound familiar? It happens all the time because no one sets expectations upfront.
What’s the fix? Just talk about it! As a mentee, be upfront about what you need (e.g., “I’m struggling with technical interviews”). As a mentor, ask open-ended questions and really listen. Together, you can map out a plan so everyone’s on the same page.
Too Much Tech Talk, Too Soon
We get it—data science is technical. But if a mentor spends an entire session on gradient boosting or convolutions without checking in, the mentee might leave more confused than inspired.
Instead, keep it actionable. Use simple examples that connect to where the mentee is now. If you’re the mentee, don’t be afraid to say, “Can we slow down?” or “How does this fit into the bigger picture?” Mentorship should feel like a conversation, not a lecture.
Forgetting About Soft Skills
Let’s be honest: being a great data scientist isn’t just about Python or SQL. You’ll need to explain your work, collaborate with stakeholders, and maybe even pitch ideas to executives. Soft skills are non-negotiable.
Mentors, share your own experiences. Talk about that time you nailed a presentation or learned the hard way how to communicate with non-technical teams. Mentees, make sure to ask about these skills. Practice mock interviews or presentations—you’ll thank yourself later.
Being Too Directive
Some mentors fall into the trap of thinking their way is the only way. They push their career path or solutions onto the mentee without considering what the mentee actually wants.
Here’s the deal: mentorship is about guidance, not control. Mentors, ask questions like, “What do you think?” or “How would you approach this?” Mentees, feel free to adapt advice to fit your unique goals. You’re driving your own career—not following someone else’s GPS.
Coming Unprepared
Have you ever walked into a mentorship session and felt like it’s going nowhere? That’s probably because someone didn’t prepare. Maybe the mentor forgot what you talked about last time, or the mentee didn’t bring any questions.
Fixing this is simple. As a mentee, jot down a few things you want to discuss. As a mentor, take five minutes to review past notes. Even a little prep can turn a mediocre session into a productive one.
Ignoring What’s Happening in the Industry
Data science moves fast. What worked five years ago might be outdated today. If mentorship isn’t grounded in current industry trends, it can feel irrelevant.
Mentors, make it a habit to stay updated. Follow industry blogs, attend webinars, or just keep an eye on LinkedIn. Share what you’re learning. Mentees, ask about emerging areas like MLOps, GenAI, or Responsible AI. Staying current helps you both grow.
Focusing Only on Short-Term Wins
It’s tempting to focus only on immediate goals like landing a job or passing an interview. While those are important, mentorship should also look at the bigger picture. What’s your five-year plan? Where do you want to grow?
Mentors, don’t just ask, “What’s next?” Ask, “What’s your ultimate goal?” Mentees, don’t be afraid to dream big and think beyond your first job. Building a sustainable career takes time and strategy.
Giving Weak or Overly Harsh Feedback
Feedback is tricky. Some mentors sugarcoat it, leaving mentees unsure about what they need to improve. Others go too hard, making the mentee feel like they’ll never measure up.
The sweet spot? Be honest but kind. Start with what’s working, then talk about what needs improvement. Mentees, don’t take feedback personally. Use it as a tool to get better. And mentors, always offer actionable steps—“Try this” works better than “This isn’t good.”
Skipping Follow-Ups
Mentorship isn’t a one-and-done deal. If there’s no follow-up, all that advice might just float away. Progress comes from consistent effort and accountability.
Mentors, check in. A quick email or message can go a long way. Mentees, update your mentor on what you’ve tried and learned since your last session. This keeps the momentum going and shows that you value their time.
Forgetting the Human Connection
At its core, mentorship is a relationship. If it feels too transactional—like a quick exchange of tips and tricks—it’s missing the magic.
Take time to connect. Mentors, ask about your mentee’s background, hobbies, or what motivates them. Mentees, don’t hesitate to share your story. When both sides genuinely care, the experience becomes richer and more rewarding.
Wrapping It Up
Mentorship is a journey, not a destination. It’s about learning, growing, and building something meaningful together. By avoiding these common pitfalls, you can make the most of your 1-on-1 sessions, whether you’re the mentor or the mentee.
So, what do you think? Have you experienced any of these mistakes? Share your thoughts—let’s keep the conversation going and help each other thrive in the ever-evolving world of data science.