Staff Data Scientist, Forecasting

Pinterest
San Francisco, CA, US; Remote, USPosted 4 March 2026

Job Description

<div class="content-intro"><p><strong>About Pinterest:</strong></p> <p>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.</p> <p>Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the <a href="https://www.pinterestcareers.com/our-life/pinflex/">flexibility</a> to do your best work. Creating a career you love? It’s Possible.</p></div><p><strong>What you’ll do</strong></p> <ul> <li><strong>Be the technical lead for the forecasting team</strong>. Own the strategy and implementation of forecasting models of key company metrics (e.g., monthly active users), delivering accurate, interpretable forecasts at scale.</li> <li><strong>Lead the full modeling lifecycle end to end</strong>: problem framing, feature engineering, model development and prototyping, experimentation and backtesting, deployment, monitoring/drift detection, and explainability.</li> <li><strong>Set the forecasting technical vision.</strong> 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.</li> <li><strong>Translate forecasts into decisions.</strong> 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.</li> <li><strong>Drive broader time‑series impact beyond point forecasts</strong>—e.g., anomaly detection, automated root‑cause analysis, campaign/channel attribution, and early‑warning signals for business health.</li> <li><strong>Embed forecasting into the business.</strong> Partner with BizOps/Finance and product teams to integrate forecasts and insights into operational rhythms, executive decision-making, and strategic planning.</li> <li><strong>Lead and mentor.</strong> Guide the work of at least two data scientists, raising the bar on technical quality, execution, and impact through candid, continuous feedback and coaching.</li> </ul> <p><br><br></p> <p><strong>What we’re looking for</strong></p> <ul> <li>8+ years of combined post-graduate academic and industry experience building and shipping production time‑series/forecasting models with web‑scale data. </li> <li>A track record of delivering adjustable, well‑calibrated, and explainable forecasting systems that informing decision-making.</li> <li>Strong background in time‑series modeling and applied statistics/econometrics; advanced degree (MS or PhD) preferred.</li> <li>Expertise in at least one scripting language (ideally Python).</li> <li>Strong SQL skills (Hive/Presto/Spark SQL) and experience building reliable data pipelines/workflows (e.g., Airflow).</li> <li>Business acumen and ownership mindset—able to simplify complex problems, connect model outputs to business levers, and prioritize for impact.</li> <li>Excellent communication skills—able to distill complex analyses and uncertainty into concise narratives for executive audiences.</li> <li>Proven technical leadership—success leading critical projects and materially influencing the scope and output of other contributors.</li> </ul> <p> </p> <p><strong>Relocation Statement:</strong></p> <ul> <li>This position is not eligible for relocation assistance. Visit our<a href="https://www.pinterestcareers.com/pinflex/"> PinFlex</a> page to learn more about our working model.</li> </ul> <p> </p> <p><strong>In-Office Requirement Statement:</strong></p> <ul> <li class="p1"><span class="s1">We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key m ... (truncated, view full listing at source)