Online collaboration platform (aka AI-powered digital whiteboard).

I develop a special kind of logic, called models, that powers digital tools to help users work more efficiently. These tools either automate routine tasks—like clustering feedback—or personalize the workspace by suggesting relevant tools and actions based on what the user needs at the moment, such as recommending a template to start with.

• Shape the goal, product, and technical limitations as well as metrics with the team
• Collect task-relevant data (without breaking privacy policies!) and clean it up—removing duplicates and inconsistencies
• Transform the data into a format that makes sense for models (usually some kind of numerical format)
• Train different models on the data and select the best-performing one
• Wrap the best-performing model into usable logic so it's ready to work in production

Hard

• Strong coding skills: at minimum, Python, Spark, and SQL
• Machine Learning knowledge: how to collect and preprocess data, understand different types of models, and optimize training
• Some (ideally more) understanding of infrastructure and software engineering: setting up data collection, storing and experimenting safely, deploying models to production, monitoring performance, and scheduling retraining

Soft

• Communication
• Collaboration
• Iterative thinking and working

Turning small, seemingly unimportant pieces of data into something valuable—by combining them to build useful features that genuinely help a specific person.

Training models is just the cherry on top. Collecting and preprocessing the data is the actual cake, with a pretty sophisticated recipe.

To build deep knowledge and gain regular hands-on experience in technical areas of Data Science, especially Data Engineering and Machine Learning Engineering.

As a former Information Architect, I’m very attentive to data—I don’t hesitate to put effort into making it consistent and truly reflect the business case we’re solving.

With a product background, I naturally think in terms of feature development workflows and user experience. That helps me prioritize better and communicate more smoothly across teams.

Technical skills related to Data Engineering (building data lakes, data warehouses, pipelines, event processing systems) and ML Engineering (model deployment options, model acceleration tricks, getting into the guts of existing models and ML-related libraries, etc.).

The extent to which I enjoy the domain I’m working in 🙂

• Databricks/Jupyter
• Snowflake
• Apache Airflow
• Miro
• VSCode

I try to group meetings together to avoid breaking up my focus time, and I block out dedicated focus slots in my calendar.

Personally, I prefer not to switch between topics or projects during the day. In general, I aim to have no more than two projects running in parallel.

Since I tend to be detail-oriented, I make a habit of stepping back once a week to reflect from a higher level. I check whether I'm still moving in the right direction, considering both my goals and timelines.

Networking is definitely important—but as an introvert, I prefer rare, deep conversations over frequent, shallow ones. I focus on topics that feel relevant either to me or to the person I’m speaking with at that moment.

I write blog posts from time to time, usually to sum up what I’ve learned or share details about a project. Writing things down helps me understand them better, and sometimes it brings the right people into my orbit and leads to great conversations.

In a fast-evolving field like Data, staying updated is crucial. But I don’t want to get “Stack Overflowed.” I try to follow a few people in the industry whose curation style and tone resonate with me.

Information hygiene is the boss 🙂

I’m lucky to have the domain I work in also be my hobby and passion. So I’d say I have a work / out-of-work work balance, which is sometimes interrupted by other unavoidable matters.

The same work, but with more time to dive deeper into hard-core technical skills and knowledge.

And some woodworking and welding—for the occasional digital detox 😉

Figure out how your brain works as early as possible—it’ll help you shape a better learning and career path.

And start proper Computer Science education right after finishing Biochemistry. That’s your thing!

I wouldn’t give advice unless someone asked for it ;)

But if I did, I’d start by asking why they think they want this kind of work, and what exactly they believe they’d enjoy. The advice really depends on those answers.

I’m not the kind of person to get easily excited by trends. I prefer to get excited by things that stay in place and become building blocks of the domain after the trend passes.

• Chat GPT, as a great counterpart to bother with stupid professional questions (while critically considering the responses, of course)
• Braun items by Dieter Rams
• Muji items
• Makita power tools
• Volvo 740 wagon

“The Information: A History, a Theory, a Flood”, a book by James Gleick; 3Blue1Brown, YouTube channel covering different math topics.

• Dima Basenka
• Vlad Zely