Data Scientist
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Prove analytical foundations and communicate assumptions
- Design experiments and build defensible models
- Turn analysis into product decisions and AI-quality evidence
Search Live Openings
We don’t have a direct job-board feed matched to this one yet, so here are real, live US job searches for “Data Scientist” instead.
Data Scientist / Product Data Scientist / Decision Scientist
Benchmark: Current DS loops: SQL, statistics, experimentation, modeling, product/business cases and communication 2026 interview refresh: Strong candidates separate descriptive, predictive and causal claims while tying analysis to decisions.
Prove analytical foundations and communicate assumptions
Full-loop mock + SQL reasoning
Practice: SQL, stats and business case under time pressure
Statistics + uncertainty
Practice: hypothesis tests, confidence and assumptions
Design experiments and build defensible models
Experimentation + causal inference
Practice: A/B design, bias, identification and validity
Modeling + evaluation
Practice: features, validation, error analysis and tradeoffs
Turn analysis into product decisions and AI-quality evidence
Product metrics + business judgment
Practice: metric trees, conflicting signals and recommendations
AI evals + executive storytelling
Practice: evaluation criteria, uncertainty and concise synthesis
Interview-Readiness Proof / Practice Loop
Complete a three-part mock: SQL case, statistics/experiment case and product/business case. Add a modeling deep dive with error analysis and a five-minute executive recommendation that clearly states uncertainty and causal limits.
Pass Rubric
- Correct SQL
- statistical coherence
- experimental validity
- correlation-vs-causation discipline
- model evaluation
- metric judgment
- uncertainty
- business relevance
- reproducibility
- clear communication