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.

Experience building user segmentation or behavioral profiling systems.

Experience in SaaS, consumer software, creator products, subscription businesses, or AI products.

Familiarity with BigQuery, Mixpanel, Statsig, Superset, Prefect, Airflow, dbt, or similar tools.

Deep data infrastructure expertise and model-training experience are not required. What Success Looks Like

Good success - Build an effective Data Science function for an AI product

Within your first 6-12 months, you will have: Systematically improved our data practices

across metric quality, experimentation, data validation, and analytical workflows.

Driven 1-2 high-impact projects

where Data contributes roughly

8%+ improvement

to an important business metric such as retention, conversion, CAC, monetization, or product adoption.

Built horizontal tools and frameworks

that help multiple Product, Growth, or AI teams deliver better outcomes without relying on repeated one-off analysis.

Functionally led a small Data group

of approximately

1 Data Scientist, 2 analysts, and 1-2 Data Engineers , raising the quality and focus of the team regardless of formal reporting structure.

Established an effective

data flywheel with our AI teams , turning product behavior into better evaluation data, feedback signals, and measurable product improvements.

Wildly successful - Turn the tide

Exceptional performance means using Data to materially change the trajectory of the company. Examples include: Discovering insights or mechanisms that enable a path toward 2x growth , such as a major improvement in retention, a substantial reduction in CAC, or a new source of monetization or product growth.

Identifying a repeatable scaling law or growth mechanism

that creates a portfolio of high-value projects capable of productively engaging roughly

30% of the company for 12+ months .

Turning Data from a support function into a

source of company strategy , consistently identifying important opportunities that Product, Growth, or AI teams would not otherwise have discovered.

Why Join Us

Shape the Data function.

You will have significant freedom to define how a modern Data team should operate.

Have direct business impact.

Your work can influence product direction, Growth investment, monetization, and AI product quality.

Build AI-native data systems.

Go beyond dashboards toward self-service, proactive insights, and agentic analytics.

Stay hands-on.

Seniority here does not mean giving up difficult analytical and technical work.

Build the AI data flywheel.

Turn real product behavior into better evaluation, measurement, and product improvement.

Bring a relatively modern stack including BigQuery, Mixpanel, Superset, Python, Prefect, and Statsig to the next level.

EEO OpusClip is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristics. OpusClip considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Opus Clip is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures.

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