Career Panel
summary
Panelists describe concrete career moves: internships turning into junior data warehouse roles, a chatbot and NLP projects (sentiment analysis, topic modeling), and building ETL pipelines to make models run in production. Hiring practices include whiteboard and take‑home coding tests, reliance on GitHub projects and Coursera portfolios, and separate roles for visualization versus engineering. The surprising, repeated claim is that machine learning products need heavy data engineering and explicit mentorship or time for learning to actually work.
AI-written summary · about
chapters (14)
- 0:00Career panel intro
- 1:25Panelists' data science journeys
- 9:23Challenges in data science roles
- 18:08Preparing for data science interviews
- 23:32Storytelling and soft skills in data roles
- 30:36Breaking into data science non-technically
- 34:24Mentorship and learning in data science
- 42:24Top skills for data science roles
- 45:18Misconceptions about data science
- 47:29Berlin hiring challenges
- 51:45Improving Berlin's data science hiring
- 56:47Q&A: Whiteboard interviews
- 1:01:27Q&A: Cross-functional knowledge
- 1:06:52Closing remarks
related talks
38mCareer Panel
27mCareer development panel: From being the only women in the room to empowering the next generation
01h 48mCareer Path Experience Stories
25mHR Panel
48mPanel - Navigating a career: tech, people, and LLMs
46mHow AI Is Transforming Data Careers
44mPanel: Career Advice for Data Scientists
56mCareer Spotlight: All you want to know about my techie job