Latest / The Comeback Career Podcast with Fexingo: Returning After a Gap, Stay-at-Home Re-Entry / Returning as a Data Scientist After a Career Gap
Transcript
- Lucas: You know, I came across a LinkedIn survey from 2024 that really caught my eye. It found that 62 percent of hiring managers for data science roles say they're open to candidates with a career gap—provided the candidate can demonstrate recent project work. Luna: That's encouraging. But I imagine a lot of parents worry their technical skills have atrophied after a few years away. How do you even start rebuilding that muscle? Lucas: That's exactly the fear—and it's valid. Data science moves fast: new libraries, updated algorithms, cloud platforms shifting. But the core concepts—statistics, linear algebra, the scientific method—are durable. The real challenge is proving you're current. Luna: So it's more about signaling than relearning from scratch. Lucas: Right. The most effective path I've seen is to pick one project and own it. A woman I know—let's call her Sarah—had a five-year gap raising twins. She enrolled in a part-time data science bootcamp that cost about 15 thousand dollars, but she didn't stop there. She spent three months building a Kaggle project predicting customer churn for a telecom dataset. Luna: Did she publish it? That's the kind of thing that can go on a resume. Lucas: Exactly. She wrote a blog post explaining her methodology, shared the code on GitHub, and even recorded a short video walkthrough. When she applied for a senior data scientist role at an insurance company, the hiring manager told her the portfolio was the reason she got an interview. She landed the job at a 10 percent pay cut from her pre-gap salary, but within two years she was earning more than before. Luna: That's a smart long-term play. I've heard some people worry that a bootcamp certificate isn't enough, but it's the combination with the portfolio that seems to work. Lucas: The bootcamp gives you structure and a network. The portfolio gives you proof. And for someone returning after a gap, proof outweighs everything. I'd also recommend contributing to an open-source project—something like the scikit-learn documentation or a dataset on Hugging Face. It shows you can collaborate in a distributed environment. Luna: Let's talk about the resume and interview. How do you frame the gap without making it the center of attention? Lucas: Short and confident. One line: 'I took a career break to focus on family.' Then immediately pivot to what you did during that time to stay sharp—even if it was just reading blogs and taking a few Coursera courses. The key is to own it, not apologize. In interviews, practice answering the question 'So what have you been doing?' in 30 seconds. Luna: And regarding certifications—I've seen the Google Data Analytics Professional Certificate recommended a lot. Is that worth the time? Lucas: It's a solid entry-level option, but for someone returning to a senior role, I'd pair it with something more advanced—like the AWS Certified Data Analytics Specialty or a deep learning specialization. The Google cert shows you care about data ethics and visualization, but for technical depth, you need something that signals hands-on coding. Luna: Honestly, if this conversation helped you think through your own re-entry strategy, that's the link—buy me a coffee dot com slash fexingo. We keep this podcast ad-free because of listeners like you who chip in a few bucks. No pressure, just gratitude. Lucas: Yeah, and we really appreciate it. So, let's shift to networking—because even the best portfolio won't help if nobody sees it. Sarah, the woman I mentioned, attended a monthly data science meetup in her city. She didn't just show up; she volunteered to help organize the next event. That got her face time with senior data scientists at local companies. Luna: That's smart—volunteering gives you a reason to talk to people without the awkwardness of 'I'm looking for a job.' Lucas: Exactly. And many meetups now have hybrid options, so even if you're home with kids, you can join remotely. Another tactic is to reach out to alumni from your university who work in data science. A simple email like 'I'm returning after a gap and would love your perspective on the field' usually gets a positive response. Luna: What about money? If you're returning after several years, you might be tempted to accept whatever offer comes. Should you negotiate? Lucas: Absolutely. But be realistic. Research the market rate using tools like Levels.fyi or the Burtch Works study. If your skills are genuinely current, you can aim for the 50th percentile. If you're a bit rusty, maybe target the 25th and negotiate based on your potential. The worst they can say is no. Luna: And if the gap is longer—say seven or eight years—does the advice change? Lucas: A bit. You might need to be more aggressive about re-skilling. Consider a longer-term program like a master's in data science or a structured returnship. Some companies like IBM and Amazon have return to work programs specifically for people with longer gaps. They provide mentorship and a gradual ramp-up. Luna: I hadn't thought about returnships—that's a great option for someone who wants a structured re-entry. Lucas: They're not as common as internships, but they're growing. And they often convert to full-time roles. The takeaway for anyone listening: your gap doesn't define your potential. What defines you is the work you do today to stay current and the story you tell about your journey. Start small—one project, one connection, one application. Luna: Well said. And if you want more resources, we'll link to Sarah's blog post and the returnship programs in the show notes at fexingo.com. Lucas: Thanks for listening, and keep building.