Episode 22: Leading Self-Driving Cars Projects with Jan Zawadzki

Show Notes:

(2:15) Jan discussed his undergraduate experience studying Business Administration and Economics from Goshen College in Indiana.(3:48) Jan went over his first job out of college: working as a Strategy and Enterprise Intelligence consultant at the EY office in Berlin, a Big 4 consulting firm.(6:09) Jan talked about his decision to pursue a part-time Master’s degree in Computer Science at the Trier University of Applied Sciences while working at EY.(7:21) Jan covered the most useful graduate courses during his Master’s degree, including Advanced Programming and Distributed Systems.(9:05) As part of his program, Jan did his thesis with the Scout24. In fact, he even wrote a blog post offering a glimpse of what it’s like to be a data scientist at Scout24.(12:31) Jan discussed the benefits of taking deep learning online classes from Andrew Ng’s deeplearning.ai platform.(15:52) Jan is also a mentor and ambassador with deeplearning.ai, in which he gives feedback on the educational content, discusses new product ideas, and writes forum entries.(19:30) Jan discussed his current projects at Carmeq GmbH, the Berlin-based innovation vehicle of Volkswagen AG.(21:22) Related to his work at Carmeq, in the blog post “The State of Self-Driving Cars for Everybody,” Jan outlined the 6 main infrastructural problems of self-driving cars for the masses. We discussed these problems in finer detail.(30:52) Jan gave a curated list of 5 mindset-chasing books that helps him become a better data scientist, referring to his article “Top 5 Business-Related Books Every Data Scientist Should Read.”(38:32) Jan shared the 5 pitfalls that young data scientists can stumble upon in their first job, referring to his article “The Power of Goal-Setting in Data Science.”(44:38) Jan emphasized the importance of using Google’s goal-setting method OKRs (Objects and Key Results) to set a data science project up for success, referring to his article “The Power of Goal-Setting in Data Science.”(48:32) Jan explained the AI Project Canvas, which answers the most pressing questions about the outcome and resources needed for an AI project.(51:50) Jan went over the importance of learning business basics for data scientists.(56:30) Referring to his post called “Becoming a Level 3.0 Data Scientist,” Jan discussed his current career trajectory as well as skills that he is looking to develop.(58:20) Jan gave his advice for data scientists to make a leap from an individual contributor to a manager.(01:02:04) Referring his post called “The Secrets to a Successful AI Strategy,” Jan gave his advice for data scientists to collaborate productively with their counterparts in product management and business operations.(01:02:55) Jan shared his opinions on the technology and data community in Berlin.(01:05:00) Closing segment.

His Contact Info:

MediumLinkedInTwitterGitHub

His Recommended Resources:

Deep Learning Specialization from deeplearning.aiFederated LearningLex Friedman’s “Deep Learning for Self-Driving Cars” class taught at MITNassim Taleb’s “Skin In The Game"Nassim Taleb’s “Black Swan"Peter Thiel’s “Zero To One"Eric Ries’ “The Lean Startup"Daniel Kahneman’s “Thinking, Fast and Slow"Richard Rumelt’s “Good Strategy, Bad Strategy"John Doerr’s “Measure What Matters"AI Project CanvasWilliam Oncken’s “Managing Management Time"Google AI and DeepMindDeepMind: The Podcast hosted by Hannah FryHans Rosling’s “Factfulness"

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

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