Hi Tip-Sheeters,

Great interview tips for your up-skilling this week, and don't miss a couple of links at the end related to a new tool worth checking out.

Creating an Up-Skilling Plan

​Andres Vourakis is a Data Scientist at Nextory and the creator of the To Be a Data Scientist newsletter, which provides a lot of technical content and career advice for data scientists.

Andres shared a post recently titled How I'm Currently Upskilling as a Senior Data Scientist in Tech (2025 Edition) with a very intentional approach to personal skill-building.

I asked him to share some more thoughts on this topic, and here is the conversation:
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Ryan: Your article is a great example of individuals making a plan for their own career growth. Is this something that you do every year?

Andres: Not necessarily. I don’t plan to do this once a year. It often happens more frequently. I usually check in with myself every six months or so, or whenever I hit a milestone or start wondering what’s next. I look at both career goals and personal ones.

It’s part of my personality to want to optimize what I do, which is probably one reason I became a data scientist. I’m constantly recalibrating, looking at how I work, how I can improve, and making sure I’m moving in the right direction.

This feels even more important now with technology evolving so quickly, especially in our field. It’s easy to get caught up in the hype around AI and all the new tools and accidentally head in the wrong direction. These regular check-ins help me stay intentional about where I am going while keeping my focus clear.

Ryan: My Tip Sheet audience loves to build skills in the data science and API space. What process could they follow to develop an up-skilling plan like this for themselves?​
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Andres: Start with a clear target. Pick one or two skills that would have the biggest impact on the kind of work you want to be doing. Don’t try to learn everything at once (otherwise you’ll set yourself up for failure).

Then build a framework that forces you to learn by doing. Watching tutorials or collecting certifications can feel productive, but it’s not the same as tackling messy, real-world problems. The real growth happens when you’re in the middle of a project that gets you stuck, forces you to troubleshoot, and pushes you out of your comfort zone.

The process is simple: choose your focus, design a project around it, and commit to finishing it, even when it gets hard. That’s where the learning sticks.

Ryan: You list a lot of great tools that are new in your tech stack. What about the other direction – are there any tools you’ve stopped using as much?​
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Andres: I don’t have any specific tools to list, but as I’ve progressed in my career, I’ve let go of the idea that I need to use the most complex or “impressive” tools to prove my skills. These days, I care a lot more about getting the job done quickly and effectively. I’ve been in situations where I spent too much time setting things up, worrying about the perfect tool, and losing focus on actually solving the problem.

Now I try to simplify. I focus on tools that help me deliver results and tell a clear, compelling story, because doing great work isn’t about how complex it looks, it’s about making the message land.

One thing I’m still figuring out is how I use AI tools. They’re a big part of my workflow now, but I’m also aware that leaning on them too much for critical thinking could hurt me in the long run. So I try to keep that in mind. I wouldn’t say I’ve found the perfect balance yet, but it’s something I’m paying attention to.

Ryan: In your post, you put a special emphasis on using your current role and employer to invest in career growth. Both with extra training and interesting projects. What advice would you give to someone who needs to convince their employer to support their professional development?

Andres: First things first, you have to ask. A lot of people stop themselves before they even start, and they do not realize how many growth opportunities are already within reach simply because they have not asked.

If you work in tech, you are in a privileged spot. You probably have access to resources, and often there is a budget for career development that has not been tapped yet.

The key is to frame your request in a way that benefits the business. Show how your development will help you deliver more value, whether that means working faster, bringing in new ideas, or helping the team stay ahead of the curve in a fast-changing field (like it’s the case for Data Science). Companies do not want to fall behind, and if you can position yourself as someone who will help prevent that, it becomes a much easier yes.

Ryan: Your mentioned three focus areas to future proof your data science career: AI-augmented analyst, ML and MLOps, and Human-First Strategist.

How did you settle on those focus areas?

Andres: I chose those focus areas by looking at two things. First, where my interests, strengths, and potential are. Second, what makes sense for the future based on how I see the field evolving.

I would not put something in my pillars if I did not see myself enjoying it or having potential to excel in it. That is not to say I would never challenge myself to start something completely new, but I try to be realistic about how much time I actually have to invest in growth.

Since I am doing this alongside my 9-5 and other projects on the side, I wanted to focus on areas where I could get the most out of my time. For me, that meant choosing skills I already have a foundation in, but that are also going to matter more and more in the future.

Ryan: In your article, you show a timeline of how you changed roles to get to where you are now as a senior data scientist. I think it’s great that you show both ups (job offers) and downs (layoffs). How can individuals stay positive and keep progressing when they have difficulties in their data science career?

Andres: In a way, it is easy for me to tell people to stay positive, stay hopeful, and be patient because I am no longer in that vulnerable place of being a beginner or a recent graduate trying to figure out my way through this field. I am not dealing with imposter syndrome in the same way I was back then. But I do have the hindsight to say there is no benefit in rushing. There is no benefit in feeling like you have to do it all extremely fast, especially when it comes to your career in tech.

The reality is that things are going to evolve whether we like it or not. There is nothing we can do to stop data science as a field from changing. This is why I was so excited to write that article, because I wanted to show that I am open to the possibilities. I think that is one of the best mindsets you can have if you want to thrive in this field.

You cannot be stuck saying all you want to do is data analysis all day. Even if that is true, the chances are if you are in this field you are curious and you probably overthink things in a good way. So tap into that. Tap into your foundation. Do not expect things to stay the same. Be open to the possibilities. I think that is the best career advice I can give to someone who is just getting started or who is in a transition point.

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Thanks so much to Andres for making time to answer my questions. Be sure and check out his To Be a Data Scientist newsletter.

A Tool Worth Checking Out

Hat tip to a Tip-Sheeter for mentioning an AI workflow tool that's been getting some buzz: https://n8n.io/. It i is a no-code tool for orchestrating agents and other AI tools. Here's a video with a more detailed explanation. I haven't found time to explore yet, but if any of you have, please pass along your thoughts.

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Keep coding,

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Ryan Day

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👉 https://tips.handsonapibook.com/ -- no spam, just a short email every week.

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