Podcast
From Postdoc to Data Science Lead: ML Foundations, Docker Deployment & Hiring Tips
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From Postdoc to Data Science Lead: ML Foundations, Docker Deployment & Hiring Tips
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Episode Overview
How do you go from a postdoc to a data science lead while mastering machine learning foundations and deployment? In this episode, CJ Jenkins — a PhD-turned-data science lead working on credit risk modeling, with published research and a textbook used in academia — walks through that transition. We trace CJ’s roots in evolutionary biology and genomics, the statistical ML foundations (GLMs, population dynamics), and practical tools like Bash, R, Python, and SQL. Key topics include Docker deployment and bridging the.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 0:58 - Career Journey: Postdoc to Data Science Lead
- 1:28 - Evolutionary Biology: Statistics & Population Dynamics
- 3:16 - Academic Research as Data Science Practice: Genomics & Bash
- 4:45 - Statistical Machine Learning: GLMs and Foundations
- 6:10 - Bridging Gaps: Deployment, Docker, and Python Learning
- 8:41 - Hiring Signals: Smartness, Ambition, and Receptiveness to Feedback
- 10:42 - Interview Assessment: Testing Learning Agility and Humility
- 11:59 - First Tech Interview: Referral, Case Study in R, and Honesty
- 15:36 - Transition Timeline: One-Year Plan and Coursera Sprint
- 17:14 - Resume Strategy: Skills-First Rewriting and LinkedIn Keywords
- 20:40 - Refining Applications: 14 CV Iterations, Recruiter Tips, and ATS
- 22:46 - Learning Resources: John Hopkins Specialization and Andrew Ng
- 25:37 - Location Strategy: Choosing Berlin and Targeting Companies
- 28:36 - Application Strategy: Selective Research vs. Volume Applications
- 31:00 - Job Move: Klarna Experience and Onboarding Challenges
- 32:48 - Internal Mobility: Relocating to Stockholm Within the Company
- 33:48 - Market Entry: Networking, Meetups, and Community Engagement
- 36:43 - Technical Expectations: Clean Code and Coding Proficiency for Juniors
- 37:39 - Skill Building: Pair Programming, LeetCode, and Code Reviews
- 40:02 - Research vs Industry: Publications, Portfolios, and Relevance
- 41:12 - Real-World Data Work: Cleaning, Bash, and Domain Translation
- 43:44 - Communication Shift: Simplifying Explanations and Office Culture
- 47:18 - Team Dynamics: Open Offices, Proximity, and Social Bonding
- 48:50 - Counterproductive Habits: Competitiveness vs. Collaboration
- 51:05 - Psychological Safety: Team Rituals, Sharing Failures, and Trust
- 52:45 - Long-Term Learning: NLP, Kaggle as a Learning Resource
- 55:28 - Academic Output: Writing a Textbook on Parasitology