Experimentation Lead, User Optimization

This is a Full Time Job

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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.

Experimentation Lead, Sign-Up & Cancellation Optimization
Team: Applied Intelligence
Company: Paramount Streaming (Paramount, Pluto TV)

About the Role
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.

What You’ll Do
Experimentation Strategy & Roadmapping
• 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.

End-to-End Experiment Ownership
• 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.

Scientific Rigor & Measurement
• 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.

Cross-Functional Management
• 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.

Basic 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.

Preferred Qualifications
• Experience in subscription businesses, streaming media, or lifecycle optimization.
• Familiarity with experimentation platforms and feature flag systems.
• Background in growth, pricing optimization, offer testing, or churn reduction strategies.
• Experience working in ML-first or AI-driven product environments.