Data Science & Analytics
Exploration, statistics, experimentation, and visualization that turn data into decisions.
Playbook: Produce an analysis stakeholders trust
From raw tables to defensible findings with documented caveats.
Exploratory Analysis
Profile distributions, missingness, leakage risks, and unit sanity before trusting any result.
Stress-test relationships with confounder thinking and simple falsification checks.
Segment users by acquisition period and trace retention/funnels without sampling traps.
Experimentation & A/B Testing
Power the test upfront, guard metrics, and commit to decision rules before peeking.
One canonical definition per metric, versioned and documented, killing dashboard wars.
Modeling Practice
Ship trivial baselines (mean, logistic, linear) to price complexity honestly.
Split-aware preprocessing, temporal boundaries, and feature provenance keeping offline gains real online.
Critical Consumption
Validity checklist catching underpowered tests, peeking, and metric tricks in published results.
Journey Playbooks
From raw tables to defensible findings with documented caveats.
A correctly powered experiment with pre-committed decision rules.
Leakage-free validation and deployment-ready artifacts.