Sr. Data Scientist, Performance Marketing

Pinterest•San Francisco, CA

22h•Remote

About The Position

Pinterest is looking for a Senior Data Scientist to join their marketing organization. This role will introduce greater scientific rigor into marketing measurement and optimization processes to shape Pinterest’s user growth and marketing strategy. The results of this work will influence and drive strategic decisions for the company, including identifying investment opportunities for growth, understanding user growth and behavior, and defining metrics to grow and sustain the user base. The Senior Data Scientist will collaborate on a wide array of business problems with cross-functional partners across Marketing, Product, Engineering, Design, Research, Product Analytics, and Data Engineering. The role is within the Growth Marketing team, supporting performance marketing efforts focused on driving user growth for Pinterest.

Requirements

5+ years of combined post-graduate academic and industry experience applying scientific methods to solve real-world problems.
Master's degree in a quantitative field such as mathematics, statistics, computer science, or engineering.
Hands-on experience 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.
Excellent communication skills, able to lead initiatives and communicate findings to leadership and cross-functional teams.
Explains work and thought processes clearly and concisely.
Experience leading key technical projects.
Strong Experimentation background.
Statistical rigor.
Experience with causal inference projects.

Responsibilities

Conduct deep strategic analysis to answer growth marketing ecosystem questions such as how to drive monthly active users from paid performance marketing, how to quantify the effect of paid performance marketing in new markets, and how to drive user engagement and user LTV (lifetime value) from performance marketing.
Evolve existing systems like geo-testing frameworks and other incrementality testing frameworks.
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 new statistical models as well as maintain/improve existing models like user lifetime value models, budgeting models, and long-term user retention models.
Collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Analytics, Marketing, Data Engineering, and others.
Evolve experimentation capabilities and tools to evaluate the business impact of performance marketing investment.
Advise on experimentation best practices, identifying flaws in experiment practices and results, and building tools for experiment analysis.
Identify the right measures of success for teams and help them track those metrics.
Own the full lifecycle of metrics from logging requirements, metrics definition, prototype pipelines, and improvements.
Translate complex analytical findings into clear, actionable insights and strategic recommendations for both technical and non-technical stakeholders, including senior leadership.
Write clear, actionable analyses that help teams uncover opportunities and identify areas of improvement to existing strategies.
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.