Sr. Data Scientist, Performance Marketing

Responsibilities

• 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, how to drive user engagement and user LTV (lifetime value) from performance marketing.
• Evolve our 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, long term user retention models etc.
• 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 our 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; building tools for experiment analysis; etc. Identify the right measures of success for teams and help them track those metrics. Own the full lifecycle of those 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 our 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.

Requirements

• Minimum 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 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.
• Excellent communication skills, able to lead initiatives and communicate findings to the leadership and cross-functional teams. Explains work and thought processes clearly and concisely.
• Experience leading key technical projects.
• Strong Experimentation background.