Staff Applied Scientist, Financial Forecasting

Responsibilities

• Architect and own end-to-end consumption forecasting ML systems across compute, bandwidth, edge functions, storage, and emerging products.
• Design and productionize advanced time-series forecasting approaches, including deep learning, probabilistic/Bayesian, hierarchical, and hybrid statistical-ML architectures.
• Develop multi-horizon forecasts for operational, quarterly, and long-range planning, reconciling predictions across account, cohort, segment, and global levels.
• Build infrastructure for backtesting, monitoring, drift detection, and forecast explainability.
• Develop scenario simulation and causal inference frameworks for pricing changes, packaging adjustments, and product launches.
• Partner with Finance leadership on board-level reporting and revenue planning, and with Infrastructure Engineering on capacity planning and cost optimization.
• Model adoption curves, expansion dynamics, and usage drivers with Product and GTM teams.
• Set ML methodology, experimentation, and measurement standards; mentor senior data scientists and ML engineers.

Requirements

• 8+ years of experience in machine learning, data science, or applied statistics, with staff- or principal-level experience.
• Advanced time-series forecasting and ML modeling expertise, including deep learning forecasting architectures, Bayesian/probabilistic modeling, and hierarchical reconciliation.
• Experience architecting and productionizing ML systems at scale, including training, serving, monitoring, and retraining infrastructure.
• Strong Python and SQL proficiency and experience with large-scale usage and billing datasets.
• Strong grounding in causal inference and experimentation design.
• Experience setting technical direction and partnering with Finance or executive leadership on planning cycles.
• Technical leadership experience, including setting standards, mentoring senior individual contributors, and influencing organizational ML practices.
• Ability to define ambiguous, high-stakes problems autonomously and communicate advanced ML concepts to non-technical audiences.
• Experience with cloud infrastructure, developer tools, or consumption-based revenue models.

Nice to Have

• Capacity planning or cost modeling experience at scale.
• Experience with Snowflake, Delta Lake, dbt, Airflow, feature stores, or MLOps tooling.
• Experience as a technical lead for a data science or ML team without formal management authority.

Compensation and Benefits

• San Francisco, CA base pay range: $250,000–$330,000.
• Competitive compensation package, including equity.
• Inclusive healthcare package.
• Mentorship and professional development opportunities, including industry events.
• Flexible time off.
• Company-provided equipment and a work-from-home budget.