Experimentation Lead SignUp and Cancellation Optimization

#We Are Paramount  on a mission to unleash the power of content... you in?

We've got the brands, we've got the stars, we've got the power to achieve our mission to entertain the planet - now all we're missing is... YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter - both for our audiences and our employees - and aim to leave a positive mark on culture.

Team:
Applied Intelligence

Company:
Paramount Streaming (Paramount+, Pluto TV)

Paramount Streaming is investing heavily in personalization, experimentation, and applied AI to shape how millions of viewers begin - and continue - their journey with Paramount+ and Pluto TV. We are seeking an Experimentation Lead to own and scale experimentation across the sign-up and cancellation flows. This is a highly strategic and execution-oriented role focused on identifying, prioritizing, launching, and scaling experiments that directly impact conversion, retention, and lifetime value.

Reporting to the Principal Machine Learning Engineer for Sign-Up Optimization, you will act as the connective tissue between product, ML, engineering, data science, design, and growth - ensuring we are running the right experiments, running them well, and learning from them rigorously. This role blends product thinking, scientific rigor, experimentation design, and operational management.

Experimentation Strategy & Road mapping

• Define and own the experimentation roadmap across sign-up, onboarding, and cancellation flows for Paramount+ and Pluto TV.
• Identify high-impact test opportunities across UX, messaging, offers, personalization, pricing, bundling, and model-driven experiences.
• Translate business goals (conversion, churn reduction, engagement, LTV) into structured, testable hypotheses.
• Prioritize experiments using data-driven frameworks balancing impact, risk, and resource constraints.

• Drive experiments from ideation → design → instrumentation → launch → feedback → iteration.
• Partner with ML engineers and applied scientists to test model-driven experiences (ranking, personalization, content ordering, offer targeting).
• Ensure experiment configurations are correctly implemented and debug issues in targeting, logging, or exposure.
• Monitor live experiments for integrity, guardrail breaches, or unintended side effects.
• Lead post-test readouts and translate results into clear, actionable next steps.

• Design robust A/B, multivariate, and sequential testing frameworks.
• Define primary metrics, guardrails, and success criteria aligned to long-term value, not just short-term conversion.
• Improve offline-to-online validation practices in partnership with ML teams.
• Standardize experimentation best practices across the Applied Intelligence group.
• Champion causal reasoning and statistical discipline in decision-making.

• Partner closely with Product, Growth, Marketing, Design, Data Science, Analytics, and Engineering.
• Serve as the central point of coordination for experimentation across the lifecycle entry/exit flows.
• Communicate insights clearly to both technical and executive audiences.
• Create scalable documentation and experiment tracking processes.

• 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…