Job Duties: Lead and Build the Marketing Data Science by setting up and growing a Data Science team supporting Pinterest's marketing operation. Develop the roadmap and execution plan for the data science teams by utilizing in-depth understanding and experience in data science and business intelligence. Drive the creation and evolution of Marketing Mix Models (MMM), Geo-Testing and Incrementality models and other statistical analyses that quantify the impact of brand and performance marketing investments. Design, prioritize, and deliver against a roadmap that quantifies and improves marketing ROI, and delivers actionable insights and recommendations to drive business objectives. Serve as a technical leader and contributor to the data science practice, by applying skills and knowledge in SQL, Python, R, and Data Modeling, as well as defining team's technical standard and owning critical analyses. Hire, coach, and develop high-performing Data Scientists and fostering technical excellence and professional growth. Collaborate deeply with Product, Engineering, Marketing, Analytics, and other Data Science teams to integrate insights into programs and product roadmaps. Build and design new tools and processes-such as recommendation engines-to uncover cost-saving strategies, optimize marketing investments, and inform executive decision-making. Serve as a trusted thought partner to senior leadership and stakeholders, communicating insights, influencing strategy, and elevating the data science profile across the company. Telecommuting and/or remote employment permitted. Minimum Requirements: Master's degree (or its foreign degree equivalent) in Quantitative Methods, Data Analysis, Quantitative Analysis or a related field and five (5) years of experience in the job offered or a related position. Special Skill Requirements: Five (5) years of experience in the following skills: 1. MySQL: Writing and optimizing MySQL/SQL queries to extract, join, and validate large-scale marketing and product datasets; building standardized datasets and metrics to support MMM, geo-testing, and incrementality measurement. 2. Python: Using Python to develop reproducible data science workflows for data preparation, feature engineering, statistical/ML modeling, and automation of analysis pipelines supporting marketing measurement and ROI optimization. 3. Statistical Analysis: Applying statistical methods to quantify marketing performance, measure uncertainty and significance, and translate results into actionable recommendations for marketing investment decisions. 4. Experimentation: Designing and analyzing marketing experiments (including geo-based tests) by defining hypotheses and success metrics, ensuring test integrity, and evaluating incremental impact to inform budget allocation and strategy. 5. Causal Inference: Estimating causal impact of marketing spend using causal inference and quasi-experimental approaches (e.g., matched markets/synthetic controls, difference-in-differences), including robustness checks and clear communication of incrementality results. 6. R: Using R to implement and iterate on MMM and incrementality models, conduct regression/time-series analyses, perform model diagnostics and validation, and produce stakeholder-ready analytical outputs. 7. Modeling: Developing and maintaining marketing measurement and ROI models by selecting appropriate methodologies, incorporating seasonality and channel interactions, calibrating/validating models, and operationalizing outputs into planning recommendations. 8. Machine Learning: Applying machine learning to build decision-support tools (including recommendation/optimization approaches) that identify cost-saving opportunities and improve marketing investment efficiency, with appropriate evaluation and interpretability. 9. Data Analysis: Performing end-to-end marketing data analysis-from problem framing and dataset creation to insight generation and executive-ready storytelling while partnering cross-functionally to embed insights into programs and roadmaps. Salary: $285,000.00 - $339,078.00 per annum Reference #: L25-172433 This position is not available for relocation assistance.