Own the strategy and implementation of forecasting models of key company metrics (e.g., monthly active users), delivering accurate, interpretable forecasts at scale.
Lead the full modeling lifecycle end to end: problem framing, feature engineering, model development and prototyping, experimentation and backtesting, deployment, monitoring/drift detection, and explainability.
Set the forecasting technical vision. Define model architectures and standards, and partner with Engineering to shape the forecasting platform for efficient training/inference today and the scalability needed for the next generation of models.
Translate forecasts into decisions. Present outputs, scenario analyses, and recommendation frameworks to senior leadership with clarity and brevity. This is a high‑visibility role with regular VP-level exposure.
Drive broader time‑series impact beyond point forecasts—e.g., anomaly detection, automated root‑cause analysis, campaign/channel attribution, and early‑warning signals for business health.
Embed forecasting into the business. Partner with BizOps/Finance and product teams to integrate forecasts and insights into operational rhythms, executive decision‑making, and strategic planning.
Lead and mentor. Guide the work of at least two data scientists, raising the bar on technical quality, execution, and impact through candid, continuous feedback and coaching.
Requirements
8+ years of combined post‑graduate academic and industry experience building and shipping production time‑series/forecasting models with web‑scale data.
Bachelor’s degree in a relevant field such as Computer Science or equivalent experience.
A track record of delivering adjustable, well‑calibrated, and explainable forecasting systems that informing decision‑making.
Strong background in time‑series modeling and applied statistics/econometrics; advanced degree (MS or PhD) preferred.
Expertise in at least one scripting language (ideally Python).
Strong SQL skills (Hive/Presto/Spark SQL) and experience building reliable data pipelines/workflows (e.g., Airflow).
Business acumen and ownership mindset—able to simplify complex problems, connect model outputs to business levers, and prioritize for impact.
Excellent communication skills—able to distill complex analyses and uncertainty into concise narratives for executive audiences.
Proven technical leadership—success leading critical projects and materially influencing the scope and output of other contributors.