Job description

Storytelling is, and will always be, the heart of what we do at Skydance. Our vision is to build a thriving entertainment ecosystem where stories and worlds are imagined, brought to life and experienced. We create for every age and audience, anchored by the strength of our content and experiences and the depth of our connection with fans. We are creator-first, audience-focused, tech-forward, globally scaled. Here, you’ll work with talented teams that move fast, think boldly and collaborate with purpose to deliver results. We aim high, champion the best ideas and win together, earning the trust of our employees, creators, partners and audiences every day. If you’re energized by the opportunity to help build the next-generation global media and entertainment company powered by creativity and technology, we’d be thrilled to welcome you aboard. LET’S GO! Overview: 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? 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. Basic Qualifications: • 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. Additional Qualifications: • 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). About Skydance Skydance is a next-generation global media and entertainment company, comprised of three business segments: Studios, Direct-to-Consumer, and TV Media. Skydance's portfolio unites legendary brands, including Paramount, Warner Bros., HBO and HBO Max, Paramount+, Pluto TV, CBS, CNN, CBS Sports, TNT Sports, Nickelodeon, Cartoon Network, MTV, Food Network, BET, HGTV, and Comedy Central. ADDITIONAL INFORMATION Hiring Salary Range: $139,000.00 - 200,000.00. In compliance with local law, we are disclosing compensation, or a range thereof, for roles where legally required. Actual salaries will vary based on a variety of job-related factors, including but not limited to geographic location, external market data, internal equity, skill set, experience, training, education, and/or performance. Base pay is just one component of our total compensation package. Other rewards may include annual bonuses, short- and long-term incentives, and program-specific awards. This position is bonus eligible. What We Offer : • Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here . • Generous paid time off. • Complimentary subscriptions to HBO Max, Paramount+, and other Skydance streaming services and digital news products. • Robust employee engagement opportunities, including access to exclusive screenings and events, speaker series, volunteer activities and more. Skydance is an equal opportunity employer (EOE) We embrace the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit. In line with the laws of the country or region in which the role is based, this includes consideration without regard to race, color, ethnicity, national origin, citizenship status, religion or belief, sex, gender, gender identity or expression, sexual orientation, age, marital status, pregnancy, parenthood, physical or mental disability, genetic information, military or protected veteran status, union status or any other category protected by applicable law. If you are a qualified individual with a disability, you may request a reasonable accommodation during the job application and/or recruitment process. You can request reasonable accommodations by calling 212.846.5500 or by sending an email to paramountaccommodations@paramount.com. Only messages left for this purpose will be returned.

Requirements

Must-haves

Storytelling is, and will always be, the heart of what we do at Skydance.
If you’re energized by the opportunity to help build the next-generation global media and entertainment company powered by creativity and technology, we’d be thrilled to welcome you aboard. LET’S GO!
The User Lifecycle Analytics team works closely with Lifecycle Marketing, Product, Finance, and DTC Executives to drive subscriber growth and retention.
The ideal candidate is detail-oriented, communicates clearly, and has hands-on experience (or eagerness to grow) in causal inference and experimentation.
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?
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.
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.
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).

Nice-to-haves

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gosqlpythonmachine learning

Platform: successfactors
Effort: medium

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