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OpusClip is an AI video agent platform that has reached 10 million creators and businesses in 18 months. The company has raised $50M from top-tier investors including SoftBank Vision Fund and DCM Ventures, and is headquartered in Mountain View with a team of 100.
You will lead OpusClip's data function as a staff-level, product-oriented data science leader. This is a hands-on leadership role where you set priorities for a small data team, personally lead the hardest analytical problems, and improve how the company measures product and business performance. You may lead through direct management or technical leadership—formal people management is not required. The role emphasizes leadership through judgment, technical depth, and example.
Key responsibilities include:
**Lead the Data Function**: Set priorities for a small data team focused on highest-impact problems. Personally lead ambiguous or high-stakes analytical projects. Raise standards for metrics, experimentation, and analytical quality. Lead and develop data scientists, analysts, and data engineers through technical direction. Turn 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 directly improves key company metrics. Build stronger user profiling and segmentation. Improve experimentation and causal measurement across product and growth.
**Improve Data Quality and Measurement**: Establish trusted definitions and validation for critical metrics. Identify systematic issues across tracking, pipelines, transformations, and dashboards. Partner with data engineering and engineering to prevent recurring data problems. Build reusable datasets, metric definitions, monitoring, and analytical frameworks.
**Build Growth Intelligence**: Help growth understand acquisition quality, retention, LTV, and channel value. Improve performance marketing measurement toward experimentation and incrementality. Identify opportunities to improve CAC, conversion, retention, or monetization. Build horizontal analytical tools and frameworks.
**Partner with AI Teams**: Support data curation, evaluation design, experimentation, and measurement for AI-powered product experiences. Identify product behavior that becomes useful evaluation data or feedback signals. Connect AI quality with real user behavior and business outcomes. Strengthen the loop from product usage to data to AI improvement.
**Build AI-Native Analytics**: Use AI to automate recurring analytical work and improve data team productivity. Build trusted self-service tools. Explore agentic systems that detect unusual metric movements and investigate causes. Move the company from dashboards toward proactive business intelligence.
Within 6–12 months, success means systematically improving data practices, driving 1–2 high-impact projects with 8%+ improvement to key business metrics, building horizontal tools that reduce one-off analysis, functionally leading a small data group, and establishing an effective data flywheel with AI teams.
The role works closely with product, growth, finance, engineering, and AI teams. You will have significant freedom to define how a modern data team operates and can influence product direction, growth investment, monetization, and AI product quality.
**Requirements:**
- Significant experience in data science, product analytics, decision science, or closely related field
- Demonstrated staff, principal, lead, or equivalent scope (regardless of formal title)
- Strong product and business judgment; ability to identify important questions independently
- Strong SQL and Python skills with 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
Note: Deep data infrastructure expertise and model-training experience are not required.
