Data Science Lead

🎨 OpusClip is the world's No.1 AI video agent, built for authenticity on social media.

We envision a world where everyone can authentically share their story through video, with no expertise needed. Within just 18 months of our launch, over 10 million creators and businesses have used OpusClip to enhance their social presence.

We have raised $50 million in total funding and are fortunate to have some of the most supportive investors, including SoftBank Vision Fund, DCM Ventures, Millennium New Horizons, Fellows Fund, AI Grant, Jason Lemkin (SaaStr), Samsung Next, GTMfund, Alumni Ventures, and many more.

Check out our latest coverage by Business Insider featuring our product and funding milestones, and our recognition as one of The Information's 50 Most Promising Startups in 2024.

Headquartered in Mountain View, we are a team of 100 passionate and experienced AI enthusiasts and video experts, driven by our core values:

• Be a Champion Team

• Prioritize Ruthlessly

• Ship fast, Quality Follows

• Obsess over customers

Be a part of this exciting journey with us!

About the Role

OpusClip is looking for a staff-level, product-oriented Data Science leader to build an effective and increasingly AI-native data function.

You will set priorities for a small Data team, personally lead our hardest analytical problems, improve how we measure product and business performance, and build systems that help teams make better decisions with less manual analytical work.

This is a hands-on leadership role. You may lead through direct management or technical leadership; formal people management is not required. We care more about your ability to lead through judgment, technical depth, and example.

You will work closely with Product, Growth, Finance, Engineering, and AI across product analytics, experimentation, user intelligence, data quality, growth measurement, AI data flywheels, and agentic analytics.

This expands the existing role from owning trusted metrics and analyses into setting direction and creating leverage across the Data function.

What You’ll Do

Lead the Data function

• Set priorities for a small Data team and focus limited capacity on the highest-impact problems.

• Personally lead ambiguous or high-stakes analytical projects.

• Raise standards for metrics, experimentation, analytical quality, and decision-making.

• Lead and develop Data Scientists, analysts, and Data Engineers through technical direction and example.

• Reduce repetitive and reactive work by turning recurring problems into reusable systems and processes.

Drive product and business decisions

• Analyze activation, retention, segmentation, monetization, user behavior, and lifetime value.

• Translate ambiguous business questions into rigorous analysis and clear recommendations.

• Identify opportunities where Data can directly improve key company metrics.

• Build stronger user profiling and segmentation to inform product strategy, operations, and company goal setting.

• Improve experimentation and causal measurement across Product and Growth.

The existing JD already emphasizes turning product and customer data into business decisions; this role owns that mandate at a broader level.

Improve data quality and measurement

• Establish trusted definitions and validation for critical product and business metrics.

• Identify systematic issues across tracking, pipelines, transformations, tables, and dashboards.

• Partner with Data Engineering and Engineering to prevent recurring data problems rather than repeatedly fixing symptoms.

• Build reusable datasets, metric definitions, monitoring, and analytical frameworks that improve self-service.

You do not need to be a data infrastructure expert, but you should be technically strong enough to diagnose how data moves through a system, identify systemic failure modes, and work effectively with engineers to fix them.

Build Growth intelligence

• Help Growth understand acquisition quality, retention, LTV, and the true value of different channels and customer segments.

• Improve performance marketing measurement beyond surface-level attribution toward experimentation and incrementality.

• Identify opportunities to improve CAC, conversion, retention, monetization, or other major business metrics.

• Build horizontal analytical tools and frameworks that enable Growth and Product teams to run better experiments and make faster decisions.

Partner with our AI teams

• Support data curation, evaluation design, experimentation, and measurement for AI-powered product experiences.

• Identify product behavior that can become useful evaluation data, feedback signals, or failure cases.

• Connect AI quality with real user behavior and business outcomes.

• Strengthen the loop from product usage → data → AI improvement → better product.

Model training experience is not required. The existing role already includes AI evaluation, curation, and online/offline measurement; this senior role is expected to make that collaboration systematic.

Build AI-native analytics

• Use AI to automate recurring analytical work and improve the productivity of the Data team.

• Build trusted self-service tools for Product and business teams.

• Explore agentic systems that can detect unusual metric movements, identify contributing segments, generate hypotheses, and investigate likely causes.

• Help move the company from dashboards and one-off analysis toward proactive business intelligence.

What We’re Looking For

• Significant experience in data science, product analytics, decision science, or a closely related field.

• Demonstrated Staff, Principal, Lead, or equivalent scope, regardless of formal title.

• Strong product and business judgment. You identify important questions instead of waiting for them to be assigned.

• Strong SQL and Python skills and a willingness to remain hands-on.

• Deep experience with product metrics, retention, segmentation, monetization, or experimentation.

• Strong understanding of statistics, A/B testing, and causal reasoning.

• Strong data-quality instincts and enough data-engineering knowledge to diagnose systemic pipeline problems.

• Ability to turn one-off analyses into reusable tools, frameworks, datasets, or processes.

• Ability to lead through influence, technical credibility, and clear communication.

• Strong ownership and effectiveness in ambiguous environments.

Nice to Have

• Growth analytics, incrementality, LTV, attribution, or causal inference experience.

• Experience working with AI/ML teams on evaluation or data curation.

• Experience building AI-assisted or agentic analytics systems.

• Data engineering experience with pipelines, transformations, backfills, or automated validation.

• Exper