Senior Data Scientist, Marketing

Zigsaw

Data Scientist in marketing at Pinterest analyzing data to drive revenue growth and marketing strategies. Collaborate with cross-functional partners utilizing AI for innovative solutions.

Posted 6/18/2026full-timeSan Francisco • California • 🇺🇸 United StatesSenior💰 $139,764 - $287,749 per yearWebsite

Tech Stack

Tools & technologies

ETLPythonSQL

About the role

Key responsibilities & impact

Analyze advertiser behavior across the end-to-end funnel to identify factors influencing acquisition, conversion, retention and churn.
Lead the design, implementation, and continuous improvement of advanced marketing measurement frameworks and statistical models to quantify marketing ROI. Marketing channels could be different paid media, email, in-product, organic social, webinars, elearning platform,in person events etc.
Build and deploy statistical and machine learning models such as propensity, forecasting, and lifetime value (LTV) models—to optimize marketing strategies and enhance audience targeting.
Develop and refine attribution methodologies to evaluate the effectiveness of marketing initiatives across multiple channels.
Assess the incremental impact of marketing activities using causal inference techniques and testing/experiment frameworks.
Conduct deep, strategic analyses to address key business questions, such as how to improve marketing ROI, how to improve marketing budget allocation to optimize the business impact, measuring the impact of marketing in both mature and new markets, identifying opportunities to increase advertiser growth and revenue through marketing initiatives
Translate complex analytical findings into clear, actionable insights and strategic recommendations for both technical and non-technical stakeholders, including senior leadership.
Design, maintain, and promote dashboards and automated reporting tools to empower stakeholders with self-serve, data-driven decision-making capabilities. Build and optimize ETL data pipelines to automate reporting, support deep dive analysis and feature engineering for analytical models.

Requirements

What you’ll need

6+ years of combined post-graduate academic and industry experience applying scientific methods to solve real-world problems.
Masters degree in a quantitative field such as mathematics, statistics, computer science or engineering.
Excellent communication skills, able to lead initiatives and communicate findings to the leadership and cross-functional teams, especially non-technical audiences. Explains work and thought processes clearly and concisely.
Hands-on experiences with building marketing measurement solutions to quantify the business impact of marketing tactics and investments.
Strong background in statistics and quantitative analysis, with experience in applying advanced statistical techniques to real-world problems.
Expertise in at least one scripting language (ideally Python/R). Proficiency in SQL/Hive. Ability to write efficient SQL queries.
Strong business and product sense. Strong skills in shaping vague questions into well-defined analyses and success metrics that drive business decisions.
Experience leading key technical projects.
Strong Experimentation background.
Statistical rigor. Experience with causal inference projects.

Benefits

Comp & perks

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Hard Skills & Tools

statistical modelsmachine learning modelspropensity modelsforecasting modelslifetime value modelsattribution methodologiescausal inference techniquesETL data pipelinesSQLPython

Soft Skills

communication skillsleadershipstrategic analysisdata-driven decision-makingcross-functional collaborationproblem-solvinganalytical thinkingclarity in explanationinitiative leadershipbusiness sense