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Mountain View, California, United States
Onsite
🎨 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!
OpusClip is looking for a product-oriented Data Scientist to help us build trusted metrics, improve data quality, and turn complex product and customer data into clear business decisions.
In this role, you will own critical definitions across feature adoption, weekly active usage, credit consumption, user segmentation, retention, and lifetime value. You will validate reporting data against its source, investigate data-quality issues, and lead analyses that help Product, Finance, and AI teams understand what is working, what is not, and where we should invest next.
This is a hands-on role for someone who is equally comfortable writing SQL, investigating unexpected data, defining metrics with stakeholders, and communicating a clear recommendation.
Validate reporting tables and dashboards against their source data.
Establish clear definitions, assumptions, sources, and owners for important metrics.
Develop monitoring for duplicate events, unexpected volume spikes, null values, stale data, and reporting discrepancies.
Investigate tracking and data-quality issues across product events and downstream reporting tables.
Help teams distinguish genuine changes in user behavior from instrumentation or pipeline problems.
Conduct post-launch analyses for major product features and workflows.
Analyze adoption, activation, funnels, retention, segmentation, and user behavior.
Translate ambiguous business questions into structured analytical plans.
Turn findings into actionable recommendations for product and business stakeholders.
Support our data analyst with complex or high-priority analytical requests.
Analyze subscriber retention, monetization, and customer lifetime value.
Compare user and revenue behavior across plans, segments, and payment platforms.
Partner with Finance to validate reporting logic and investigate discrepancies.
Communicate assumptions and data limitations clearly when working with financial and payment-related data.
Support data curation, measurement design, and evaluation for AI-powered product experiences.
Define offline and online success metrics for model-driven features.
Analyze model quality, user behavior, and post-launch business impact.
Partner on experimentation, segmentation, and ongoing performance monitoring.
Typically 3+ years of experience in data science, product analytics, decision science, or a closely related field—or equivalent experience owning work at this level.
Advanced SQL skills, including complex joins, window functions, event-level analysis, cohort analysis, and query debugging.
Strong Python skills for analysis, automation, validation, and statistical work.
Experience analyzing product usage, feature adoption, funnels, retention, segmentation, or monetization.
Experience defining and governing metrics, rather than only using existing definitions.
Strong data-validation instincts, including checking joins, duplication, nulls, freshness, source consistency, and tracking changes.
Experience working with event data from Mixpanel, or a similar product analytics platform.
Experience building or maintaining dashboards using tools such as Superset, Looker, Tableau, or Power BI.
Working knowledge of experimentation, statistical inference, and the limits of causal conclusions.
Strong ownership and the ability to make progress when requirements are ambiguous.
Clear communication skills and experience partnering with both technical and non-technical stakeholders.
Experience with BigQuery or another cloud data warehouse.
Experience in a product-led SaaS, subscription, consumer software, creator-economy, or AI product company.
Experience analyzing subscription payments, retention, revenue, or LTV.
Familiarity with Stripe, Apple, Google Play, PIX, or other payment-platform data.
Experience with automated data-quality monitoring or anomaly detection.
Experience partnering with ML or AI teams on model evaluation, data curation, or online experiments.
Familiarity with semantic layers, metric stores, or governed self-service analytics.
Exposure to orchestration and transformation tools such as Prefect, Airflow, or dbt.
Awareness of data governance, PII protection, and column-level access controls.
Experience using AI tools to accelerate analytical work while independently validating the output.
Within your first six months, you will have:
Established trusted definitions for our priority product and business metrics.
Restored or improved reporting for feature adoption, WAU, and credit consumption.
Validated critical reporting tables against their source data.
Introduced proactive monitoring for high-impact data-quality issues.
Delivered product analyses that directly informed roadmap, launch, or experimentation decisions.
Improved the reliability and turnaround time of analytical support for Product, Finance, and other teams.
Become a trusted analytical partner who can move from an ambiguous question to a clear, evidence-based recommendation.
Own metrics and analyses that directly influence product and business decisions.
Work closely with Product, Engineering, Finance, CX, and AI teams.
Help shape the analytical foundation of a fast-growing AI product.
Work with a modern data stack that includes BigQuery, Mixpanel, Superset, Python, Prefect, and Statsig.
Tackle a mix of product analytics, data quality, experimentation, monetization, and AI evaluation.
Grow toward broader ownership in product data science, experimentation, metric governance, or analytical leadership.
If you enjoy finding the truth behind the numbers—and making that truth useful to the people building the product—we’d love to hear from you.
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.
