You'll serve as the technical backbone for our growing data science team. You'll bring rigorous statistics and machine learning expertise and help us evolve from notebook-based exploratory work into centralized, production-grade data pipelines. This is a hands-on individual contributor role with both ownership of process and technical mentorship responsibility. What you'll own (and how we measure it) Technical quality and methodology • Bring statistical rigor and ML expertise to a team that's currently strong on exploration but light on formal methodology. Own the technical quality bar for models, metrics, and analyses across the team. • Help define what "good" looks like technically: code review standards, reproducibility practices, and a clear path from prototype to production. • Stay current on modern ML and agentic workflow techniques and help the team adopt them where relevant. • Contribute hands-on to real analyses and models. This is not a purely advisory seat; it's a working technical role. Production infrastructure • Help migrate our work from ad hoc notebooks into centralized, version-controlled, reproducible workflows and shared artifact storage. • Design and implement production-grade data pipelines on GCP: scheduled and triggered jobs, not just one-off notebook runs. Mentorship and team growth • Mentor data scientists across the team at varying experience levels, from junior to mid-level, raising the technical bar on statistics, ML, and production practices. • Partner with the DS team's manager (SVP of Product & Data) on technical direction while they focus on enterprise strategy and vision. The reality of this role • Small, senior, distributed team: you help build the practice, you don't inherit one. • This is multi-project work that varies widely in scope and domain, so you'll move between problems rather than specializing narrowly. What we're looking for Must-haves • 6-8+ years of professional data science experience, with a strong formal foundation in statistics and machine learning. You're comfortable owning methodology decisions, not just running existing scripts. • At least 2-3 years of hands-on production/deployment experience, not just modeling in isolation. • Experience productionalizing data science work: moving from notebooks to scheduled, reliable, production pipelines. • Hands-on experience with GCP (BigQuery, Cloud Composer, Cloud Functions, or similar); comfort designing cloud-based scheduled and triggered workflows. • Strong Python skills across the full DS workflow: ETL, analysis, and modeling. • Experience mentoring or technically leading less experienced data scientists. • You've thrived in a startup, small, or other 1-10 environment where you built structure from scratch and owned the outcomes. Helpful, not required • Data engineering background. • Experience with agentic or LLM workflows. • Data analytics or BI tooling experience.