Senior Data Scientist (Growth Marketing)

Join our team as a Senior Data Scientist focused on Growth Marketing. In this role, you will design, deploy, and refine predictive models to guide multi-channel marketing budget allocation. You will conduct deep-dive cohort analyses and retention modeling, lead the growth experimentation roadmap, and partner with Growth Marketing and Finance leaders to optimize unit economics. You will also collaborate with analytics and data engineering teams to establish event tracking requirements and build scalable growth data pipelines. This position offers a hybrid work environment and a comprehensive benefits package.

• Design, deploy, and refine predictive models for Multi-Touch Attribution (MTA), Customer Lifetime Value (CLV), and Customer Acquisition Cost (CAC) efficiency.
• Conduct deep-dive cohort analyses and retention modeling to uncover drivers of user conversion, repeat behavior, and churn.
• Lead the growth experimentation roadmap by designing, executing, and analyzing A/B tests, geo-experiments, holdout tests, and incrementality studies.

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field
• 6+ years of proven experience as a Data Scientist or Analytics Lead, with a distinct focus on growth marketing, performance media, or adtech
• Strong business acumen with a track record of partnering with senior executives in Growth, Product, and Finance to drive measurable bottom-line growth
• Excellent analytical, problem-solving, and communication skills to make complex data stories intuitive and actionable
• Advanced proficiency in Python and SQL for complex data manipulation, statistical modeling, and predictive analytics
• Hands-on experience with statistical experimentation and causal inference techniques such as A/B testing, geo-testing, synthetic controls, and media mix modeling
• Deep domain expertise in growth measurement frameworks, multi-touch attribution, cohort retention modeling, and LTV/CAC dynamics
• Experience leveraging AI tools to transform static workflows into responsive, high-output processes
• Experience with cloud platforms, including AWS S3, SageMaker, and Airflow
• Experience with cloud data warehouses and modern data tools such as Snowflake, dbt, Looker, and Tableau
• Familiarity with Media Mix Modeling (MMM) frameworks including Robyn and LightweightMMM
• Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch applied to user segmentation or churn prediction
• Ability to write clean, efficient, robust, and well-commented code to produce high-quality data science procedures