Jenna Hunte on Mentorship, AI, and Supporting the Next Generation of Tech Talent at Flatiron School

Jenna Hunte brings over a decade of engineering and tech education experience to her role as a mentor in Flatiron School’s Data Science program. After starting her career in software engineering and moving into AI consulting and engineering leadership, she now plays a key role in helping bootcamp students navigate a fast-changing industry. We caught up with Jenna to learn more about her unique mentorship style, how she’s supporting career changers in the AI era, and what bootcamp grads can do to stand out in technical interviews.

The mentor role is relatively new at Flatiron School – can you explain what it means to you?

The mentor role adds a human component to the learning experience. With so many online resources, it’s easy to feel overwhelmed or isolated. A mentor can offer real-world context, help you make sense of conflicting information, and answer the classic: “How does this apply to what I want to do in the real world?”

I’ve learned a lot through formal education, but what truly made me a strong engineer was the time others spent mentoring me. That’s what I try to pass on – making the content more digestible, applicable, and less intimidating.

Over the past decade, I’ve worked with K–12 schools and taught part-time online. Teaching has always been my passion, so balancing mentorship with a full-time job comes naturally to me. 

What does mentorship look like in practice for you at Flatiron?

I mentor a cohort of four to six students in the Data Science Bootcamp. It’s a four-month course that covers data engineering, data science, and AI, so it aligns with my background.

I love that I usually work with the same students through each program phase. We meet twice a week for 1.5 – 2 hours. Each session starts with a check-in to gauge how the student is doing with the fast-paced curriculum. Then, we tackle any challenges they face together, creating psychological safety and letting students learn from each other.

After that, I’ll often introduce real-world tech updates or demo tools not covered extensively in the curriculum, like Docker or agentic workflows, to help students stay current and connect the dots between theory and application.

What kind of support do students typically ask for – curriculum help, industry advice, career coaching?

All of the above! It depends on the week and the student. Some need help understanding SQL one day and want to talk about AI trends the next. I encourage my students to ask how their learning connects to work they want to do in the real world. I dedicate time to discuss job readiness and what hiring managers are prioritizing.

Speaking of trends, what’s your take on the “AI is replacing junior developers” narrative?

I firmly believe that AI will replace some jobs and enable humans to do things we can't even predict! 

What I tell my entry-level students or recent grads is: don’t get caught up in the fear. Instead, figure out what you’re passionate about and follow that. AI and tech are going to touch every single industry. My passion is education and teaching, so I focus on how AI applies to that space – what tools can improve learning, how we use technology in schools, and how to make those systems more equitable. If your passion is healthcare, maybe you’ll work on AI models that detect oral cancer from medical images. If it’s finance or real estate, the opportunities are just as real.

Yes, the exact job you envisioned when you started bootcamp might not be the one you get. But if you keep learning, stay curious, and build your understanding of the core concepts – how things work together, how to think like a technologist – you will remain relevant, and you will succeed.

Now, the students who won’t be successful are the ones who approach bootcamp with the wrong mindset. I’m talking about the people who submit every assignment to ChatGPT and let AI do the work for them. 

To be clear, I support using AI tools to deepen your understanding. If you're learning Python and stuck on for-loops, prompt the AI and ask it to explain. That’s a great use case. But I’ve had students who rely on AI to complete assignments without trying to understand the content, and when I speak with them, they’ve retained nothing. That approach doesn’t work in a changing field as fast as tech. The gaps will show up in interviews and on the job.

So, how should a bootcamper balance learning the fundamentals vs staying up-to-date on the latest tech trends?

Learn the fundamentals first. If you don’t know how to trace a function or build a pipeline, you won’t know when your AI tools give you the wrong output.

That said, tech isn’t a static field – you must love learning to succeed here. I built my career on breadth rather than depth. I try to stay informed enough to follow conversations and identify emerging patterns. If something sparks my interest, then I go deep.

Students can do the same. Either specialize in something like agentic workflows, or stay broad and adaptable. But either way, you can’t skip the basics.

From your perspective as a hiring manager, what can a bootcamp grad do to stand out in a technical interview?

Authenticity and clarity. I don’t need you to know everything – I need you to understand what you know and admit what you don’t. I’ve interviewed many people who relied on AI tools during interviews. They can list all the right technologies, but they can’t connect them or explain their decisions.

I would rather hire someone who says, “I’m not sure – I’ll look into that and follow up.” If they follow up after the interview with a thoughtful response, I’m wildly impressed. That shows curiosity, integrity, and the ability to grow.

What kind of roles are your students prepared for when they graduate?

Most are prepared for entry-level data science or data engineering roles, depending on where they want to specialize. Some may lean into AI product roles, especially if they come from a relevant domain like healthcare or finance. But ultimately, they’re learning how to work with data – how to analyze it, build models, understand infrastructure – and that skillset is incredibly flexible.

You broke into tech with a traditional computer science degree. Do you think the non-traditional path can work?

Absolutely. It’s not about your degree – it’s about your commitment to learning. I’ve hired incredible engineers with no formal background who taught themselves everything. What matters is whether you’ve taken the time to understand how things work – and whether you can work with others and grow.

You’ve worked with students from all walks of life – real estate, healthcare, and finance. How do you tailor your mentorship for career changers?

I use examples from their industries whenever possible. Tech is just a tool – it’s most powerful when applied to something you care about. If someone comes from healthcare, we’ll talk about AI models in diagnostics. If they come from real estate, maybe it’s predictive modeling for home prices. Context makes the learning stick.

I can usually tell early on who’s going to succeed and who’s going to struggle. The ones who engage, ask questions, and stay curious – they’re the ones I bet on.

Are there any AI certifications you recommend for job-seekers?

I’m not a big fan of broad AI certifications right now – they’re outdated almost as soon as they’re issued. That said, if you want to specialize in a particular platform, certifications can help. Databricks, AWS, Azure, and Snowflake all have solid certs that show you’ve invested in learning that ecosystem. But a generic “AI certification” doesn’t carry much weight yet.

You’ve mentioned that impostor syndrome was a challenge early in your career. What advice would you give to students who feel like they don’t belong in tech?

First, know that everyone feels that way at some point – even senior engineers. Second, don’t try to know everything. Know your strengths, admit your gaps, and commit to learning.

If you’re genuinely curious and committed to growth, you’ll earn your place. I tell my students that no one expects them to be perfect. What matters is your willingness to ask questions, follow up, and keep improving.

Find out more and read Flatiron School reviews on Course Report. This article was produced by the Course Report team in partnership with Flatiron School.