Staff Data Scientist, Forecasting

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

What you’ll do:

• Be the technical lead for the forecasting team. 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, executi