Lead Data Scientist, Forecasting & Time-Series (Equity)

Pinterest; California (MO); Hybrid; USD 165,000 - 339,000; Full time

Job summary

Pinterest is seeking a technical lead for the forecasting team to own strategy and implementation of forecasting models for key metrics (e.g., monthly active users), delivering accurate forecasts at scale.

You will lead the full modeling lifecycle end‑to‑end: problem framing, feature engineering, model development and prototyping, experimentation and deployment, with a focus on explainability and scalability. This role involves mentoring data scientists and partnering with BizOps/Finance.

Qualifications

• 8+ years of post-graduate and industry experience in time-series forecasting with web-scale data.
• Proven ability to build adjustable, well-calibrated forecasting systems that inform decisions.
• Strong background in statistics/econometrics; MS or PhD preferred.
• Expertise in Python and SQL; experience with data pipelines (Airflow) is a plus.

Responsibilities

• Be the technical lead for the forecasting team, owning strategy and implementation of models for key metrics.
• Lead the full modeling lifecycle end-to-end: framing, feature engineering, development, experiments, deployment and monitoring.
• Set the forecasting vision, define architectures, and collaborate with Engineering on platform scalability.
• Translate forecasts into decisions; present outputs and scenarios to senior leadership with clarity.
• Drive time-series impact beyond point forecasts: anomaly detection, root-cause analysis, attribution.
• Embed forecasting into business rhythms with BizOps/Finance and product teams, guiding strategic planning.
• Lead and mentor at least two data scientists, elevating quality and impact through feedback.

Skills

Forecasting models

Time-series modeling

Python

SQL (Hive/Presto/Spark)

Leadership

Education

MS/PhD preferred

Tools

Airflow

Forecasting platform

Job description

Pinterest is seeking a technical lead for the forecasting team to own strategy and implementation of forecasting models for key metrics (e.g., monthly active users), delivering accurate forecasts at scale.

You will lead the full modeling lifecycle end‑to‑end: problem framing, feature engineering, model development and prototyping, experimentation and deployment, with a focus on explainability and scalability. This role involves mentoring data scientists and partnering with BizOps/Finance.