Opusclip Staff-level Data Science leader at OpusClip to build an AI-native data function and lead analytical projects. Responsible for setting priorities, improving metrics, and enabling data-driven decisions across teams.
Responsibilities
OpusClip is looking for a staff-level, product-oriented Data Science leader to build an effective and increasingly AI-native data function.
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.
This expands the existing role from owning trusted metrics and analyses into setting direction and creating leverage across the Data function.
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.
Qualification
Significant experience in data scienceDemonstrated StaffStrong product and business judgmentDeep experience with product metricsStrong understanding of statisticsAbility to lead through influenceGrowth analyticsExperience in SaaSFamiliarity with BigQuery
Required
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.
Preferred
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.
