Lead Data Scientist

Paramount•New York, NY

6d•$139,000 - $200,000

About The Position

We are the Global Content and Lifecycle Analytics team, part of the Paramount Streaming, Data & Insights Group (DIG)! DIG is a key connector across the Paramount Streaming verticals. Our group is made up of subject matter experts who prototype, build, and scale data infrastructure and products. We also assess, aggregate, and analyze data, and we shape both qualitative and quantitative stories and insights. In short, we give our stakeholders decision support, clear performance views, and recommendations that help the business grow! The User Lifecycle Analytics team works closely with Lifecycle Marketing, Product, Finance, and DTC Executives to drive subscriber growth and retention. The Lead Data Scientist reports to the Senior Director of Data Science. You'll support a wide range of projects, partnering with the Lifecycle Marketing and Product teams, DTC executives, and Finance. This role calls for a solid analytical foundation, real curiosity about subscriber behavior, and the skill to run careful analysis that feeds straight into strategy! The ideal candidate is detail-oriented, communicates clearly, and has hands-on experience (or eagerness to grow) in causal inference and experimentation. This role supports two core business questions: What drives long-term subscriber health, and how do we use our levers (lifecycle, product, pricing, promotions) to grow it? How do our optimization efforts across content and the subscriber journey affect the bottom line?

Requirements

Bachelor's degree in a quantitative field (Statistics, Economics, Data Science, Computer Science, Operations Research, or related).
5+ years in data science/analytics, with hands-on use of causal inference or experimentation methods (e.g., A/B testing, diff-in-diff, propensity score matching) — not just predictive modeling.
Experience designing or analyzing A/B tests, including an understanding of common pitfalls (novelty effects, selection bias, sample ratio mismatch).
Solid SQL skills and proficiency in Python or R for statistical analysis.
Experience working with or building LTV, survival, or churn-related metrics.
Great communication skills, with the ability to turn analytical results into clear takeaways for non-technical stakeholders.

Nice To Haves

Experience in subscription/DTC streaming, media, or another subscription business (e.g., SaaS, telecom).
Familiarity with subscriber/customer journey mapping.
Exposure to advanced causal techniques (e.g., instrumental variables, double machine learning, causal forests).
Familiarity with cloud data warehousing/BI tools (e.g., BigQuery, Databricks, Looker, Tableau).

Responsibilities

Run causal inference analyses (e.g., diff-in-diff, matching, uplift modeling) under the Sr. Director's guidance. You'll find which subscriber behaviors truly drive retention and long-term value, while correcting for known biases in streaming data.
Help map the subscriber journey to pinpoint where lifecycle, product, pricing, and promo levers have the most impact.
Help design, set up, and analyze A/B tests across the subscriber journey (acquisition, onboarding, engagement, win-back), including power analysis, monitoring, and read-outs.
Build and maintain reporting and models that turn journey optimization into ARPU, survival, and LTV metrics for stakeholders.
Own the SQL/Python data pulls, cleaning, and analysis pipelines behind the team's causal and LTV modeling.
Prepare clear, well-organized presentations and summaries of your findings for stakeholders.
Partner with Data Engineering and Product Analytics to check data quality and fix issues in our experimentation and behavioral data pipelines.
Stay current on causal inference and experimentation best practices and help promote sound methods across the team.
Mentor junior analysts on SQL, statistical methods, and analytical best practices as the team grows.