Data Science Lead

Opus Clip 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 Opus Clip 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 Soft Bank Vision Fund, DCM Ventures, Millennium New Horizons, Fellows Fund, AI Grant, Jason Lemkin (Saa Str), 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!

Opus Clip 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.

• 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.

• 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.

• 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.

• 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.

• Significant experience in data…