Front is looking to build trustworthy, interpretable models and analyses that power executive decisions across Finance and GTM by turning revenue, pipeline, product usage, and customer lifecycle data into clear insights and forward-looking signals to drive retention, unit economics, and efficient growth.
Requirements
- Strong SQL and data modeling experience in a modern stack (Snowflake, dbt); proficiency with Python or R.
- Deep statistical knowledge, including experimental design, causal inference, time series analysis, hypothesis testing, sampling, and uncertainty quantification.
- Expertise with revenue metrics and cohorts: ARR, GRR/NRR, churn, expansion, pricing and packaging impacts.
- Proven experience building interpretable predictive models and health scores tied to business actions.
- Ability to define metrics rigor, resolve survivorship/definition issues, and ship executive‑grade dashboards.
Responsibilities
- Owning end‑end analytics and modeling for ARR, GRR/NRR, churn and expansion, pipeline health, and forecasting.
- Designing contraction/retention diagnostics and customer health scoring with clear business levers.
- Building robust data sets and metrics definitions across Snowflake/dbt, ensuring quality and consistency.
- Translating exec questions into analyses, KPIs, and narratives that inform targets and pacing.
- Partnering with RevOps, Finance, and Product to instrument experiments and revenue initiatives.
- Communicating trade‑offs, assumptions, and recommended actions with crisp, decision‑ready storytelling.
Other
- 6+ years in data science or analytics focused on Finance or GTM at a SaaS or B2B company.
- Excellent communication and stakeholder partnership skills, from ICs to executives.
- Front operates on a hybrid model — we come together in the office each Tuesday, Wednesday, and Thursday to collaborate and stay connected.
- Flexibility to work from home Monday and Friday, unless posted as a fully remote role.
