Data Science Lead

About the Role

OpusClip is looking for a staff-level, product-oriented Data Science Lead to lead a team of approximately five: two Data Scientists, two analysts, and one to two Data Engineers. Title will be calibrated to experience.

This is a hands-on role: you will set the Data roadmap, personally tackle our hardest analytical problems, and build an increasingly AI-native data function. You can lead through technical direction or direct management; formal people management is not required.

You will work closely with Product, Growth, Finance, Engineering, and AI to turn trusted data into better product and business decisions.

Success means delivering measurable business improvements while building the systems and practices that let a small Data team support a growing company.

What You’ll Do

Lead the Team and Strengthen the Data Foundation

• Set priorities, develop the team through technical direction and example, and focus capacity on the highest-impact problems. Personally lead ambiguous, high-stakes analyses.
• Own standards for metric definitions, data validation, and analytical quality. Partner with Engineering to diagnose and prevent recurring issues across tracking, pipelines, transformations, and dashboards.
• Turn recurring requests into reusable datasets, frameworks, and self-service tools so teams can make sound decisions with less manual support.

Drive Product and Growth Decisions

• Use behavioral analysis, user profiling, and segmentation to identify opportunities across activation, retention, monetization, and lifetime value. Translate findings into recommendations that inform product strategy, operations, and company goals.
• Strengthen experimentation and causal measurement across Product and Growth. Evaluate acquisition quality and the long-term value of different channels and customer segments, moving beyond attribution toward incrementality to guide investment and improve CAC.

Connect Product Data to AI Improvement

• Partner with AI teams on data curation, evaluation design, and online and offline measurement. Turn product behavior into useful evaluation data, feedback signals, and failure cases.
• Connect changes in AI quality to user behavior and business outcomes, creating a measurable loop from product usage to AI improvement and better product experiences.

Build Ai-native Analytics

• Use AI to automate recurring analysis and explore agentic systems that detect unusual metric movements, identify contributing segments, and investigate likely causes.
• Make these workflows reliable enough for teams to use, moving from one-off requests toward proactive insights with clear validation and human judgment.

What We’re Looking for

• Significant experience in data science, product analytics, decision science, or a related field, with demonstrated Staff, Principal, Lead, or equivalent scope, regardless of title.
• Strong product and business judgment: you identify important questions, navigate ambiguity, and turn evidence into decisions.
• Strong SQL and Python skills and a willingness to stay hands-on.
• Deep experience with product metrics and user behavior, plus a strong foundation in statistics, A/B testing, and causal reasoning.
• Strong data-quality instincts and enough engineering knowledge to trace data through a system, diagnose recurring pipeline problems, and work effectively with engineers on durable fixes.
• Ability to lead through influence, technical credibility, and clear communication across technical and business teams.

Deep data infrastructure expertise and model-training experience are not required.

Nice to Have

• Experience measuring paid marketing incrementality and acquisition economics.
• Experience partnering with AI/ML teams on evaluation or data curation, or building AI-assisted or agentic analytics systems.
• Experience in SaaS, consumer software, creator products, subscription businesses, or AI products.

Within your first 6-12 months, you will have

• Delivered measurable business impact: led 1-2 high-impact projects where Data contributes roughly 8%+ improvement to a key metric such as retention, conversion, CAC, monetization, or product adoption.
• Built a more effective Data function: improved metric quality, experimentation, and validation while raising the team's focus and analytical standards.
• Made the team's work go further: shipped reusable tools that help Product, Growth, or AI teams achieve better outcomes without repeated one-off analysis.
• Established a measurable AI feedback loop: turned product behavior into better evaluation data and demonstrable product improvements.

Beyond these initial milestones

• Expand your influence and scope: shape company strategy by uncovering major growth opportunities, with room to take on broader Data leadership as the team and business scale.