Experimentation Lead, Sign-Up and Cancellation Optimization
$156,800–$235,200 year
On-site · California, United States or New York, United States
Job Summary
Experimentation Lead, Sign-Up & Cancellation Optimization at Paramount Streaming drives end-to-end experimentation across sign-up, onboarding, and cancellation flows for Paramount+ and Pluto TV. You will define and own the experimentation roadmap, identify and test high-impact opportunities across UX, messaging, offers, personalization, pricing, and model-driven experiences, translate business goals into testable hypotheses, and partner with ML engineers to validate model-driven experiences. You’ll design robust A/B, multivariate, and sequential tests, monitor live experiments, lead post-test readouts, and standardize best practices across the organization while collaborating with Product, Growth, Marketing, Design, Data Science, Analytics, and Engineering.
Required Qualifications
- 3–5+ years of experience in experimentation, growth, product analytics, applied ML, or related quantitative roles
- Proven track record designing and executing high-impact A/B tests in high-scale consumer digital products
- Deep fluency in experimentation methodology: statistical inference, power analysis, guardrails, sequential testing, causal reasoning
- Experience working closely with ML-driven systems (recommendation, ranking, personalization) in production environments
- Strong analytical toolkit (SQL required; Python/R preferred)
- Ability to debug experiment integrity issues (logging, targeting, traffic splits, instrumentation gaps)
- Strong product intuition and ability to translate ambiguous business questions into structured testable frameworks
- Excellent stakeholder management skills and executive-level communication
Desired Qualifications
- 3–5+ years of experience in experimentation, growth, product analytics, applied ML, or related quantitative roles
- Proven track record designing and executing high-impact A/B tests in high-scale consumer digital products
- Deep fluency in experimentation methodology: statistical inference, power analysis, guardrails, sequential testing, causal reasoning
- Experience working closely with ML-driven systems (recommendation, ranking, personalization) in production environments
- Strong analytical toolkit (SQL required; Python/R preferred)
- Ability to debug experiment integrity issues (logging, targeting, traffic splits, instrumentation gaps)
- Strong product intuition and ability to translate ambiguous business questions into structured testable frameworks
- Excellent stakeholder management skills and executive-level communication
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