Use quantitative analysis to understand what drives Bitstack’s business and product performance.
Build full-cycle analytics work: datasets, dashboards, and executive-ready narratives.
Create repeatable analytical frameworks (cohorts, segmentation, measurement plans) rather than one-off answers.
Partner with Engineering to implement, validate, and monitor event logging and data quality.
Work cross-functionally with Product, Engineering, Compliance/Risk, Ops, Support, and Finance to scope ambiguous problems and turn them into data-backed decisions.
Proactively identify levers to move key metrics, size opportunities, and recommend actions.

Strong quantitative foundation in Statistics, Economics, Math, CS, Physics, or equivalent practical experience.
Expert SQL and comfort working directly with production-grade datasets.
Strong Python (or R) for analysis, modeling, and automation.
Experience with experimentation, hypothesis testing, and causal thinking (A/B testing, quasi-experiments when A/B is not feasible).
Experience with analytical methods such as: Funnel analysis and product measurement, Cohort retention, segmentation, and lifecycle analysis, Time-series analysis and forecasting, Regression / classification, model evaluation, and pragmatic deployment patterns
Proven ability to take ambiguous problems and solve them in a structured, hypothesis-driven way.
Strong stakeholder communication: you can turn messy analysis into clear recommendations and trade-offs.

Data Analytics Lead

Key skills