FOR REASONS I’m reading a lot of startup literature right now, including Zero to Sold, The Mom Test, Four Steps to the Epiphany, and The Lean Startup. One thing all of these books have in common is that they say the old line of “build and they will come“ is wrong. Considering myself a technical … Continue reading A Plea for the Technical Founder
As machine learning is becoming more mainstream (well that’s already long past I guess) more and more teams who are new to ML are attempting to run data science projects. One of the most common mistakes is to think that ML is “just another library” so that people are approaching a data science project like … Continue reading Machine Learning As The Ultimate Test Driven Development
I always felt that Data Science and Product Management share a lot of research aspects. When developing a new product, you have to work with assumptions, uncertainties, and gradually figure out a path to validate or verify these assumptions. You need to adapt till you have found something that solves a relevant problem for the … Continue reading What is Data Science/Product Fit And Why You Need It
Running data science projects successfully is already quite challenging, and the first step is to hire some data scientists. Here are my answers to questions I often get asked.
I’m re-reading Inspired by Marty Cagan lately and came across this quote: “To set your expectations, strong teams normally test many product ideas each week-on the order of 10 to 20 or more per week.” To be honest I was pretty shocked.
People who ask me “how do you solve a certain project with machine learning” often expect some kind of a recipe as if they were baking a cake. I understand the expectation and often find myself trying to give something as close to a recipe, but lately I have come to realize that the answer is more a process than a recipe.
I have a hunch that once people saw the economic potential of software, they started looking for ways to "scale it up" and we haven't stopped searching yet.
Based on this twitter thread. One thing that always makes me think are the roles we have in the software industry. The bigger the company, the more specialized the roles it seems. In data science, I‘ve seen charts with data analysts, data scientists, ML engineers, MLOps, ... some thoughts. How do you come up with … Continue reading Don’t Do Roles (Only) By Skillset
If you're a senior software engineer or data scientist, you might wonder what the next career step is. Some companies offer an "individual contributor" track, which is called principal, or staff, and that may seem like an attractive alternative to becoming a manager. I have worked in this role both at Zalando and now GetYourGuide, … Continue reading Some Thoughts on the Principal Role
Cleaning up my bookshelf a bit, I came upon the book Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. It deals with all kinds of ways to deal with big data sets, data streams, link structures between documents, social network analysis, and other kinds of data which of occur in large amounts. There … Continue reading When Big Data became Scalable Databases