Data Science Lead

OpusClip•Mountain View, CA

2d•$230,000 - $300,000•Onsite

About The Position

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.

Requirements

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 Haves

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.

Responsibilities

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.
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.
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.
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.