{"id":1938101,"url":"https://alion.io/job/opusclip-data-science-lead","title":"Data Science Lead","company":{"id":63348,"name":"OpusClip","domain":"opus.pro","url":"https://alion.io/company/opusclip","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":230000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"BigQuery","optional":true},{"name":"dbt","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Prefect","optional":true},{"name":"Superset","optional":true}],"status":"live","first_seen_at":"2026-10-06T00:21:00Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-08T02:01:27Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"OpusClip is the world's No.1 AI video agent, built for authenticity on social media.\nWe 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.\nWe 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.\nCheck 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.\nHeadquartered in Mountain View, we are a team of 100 passionate and experienced AI enthusiasts and video experts, driven by our core values:\nBe a Champion Team\n\nPrioritize Ruthlessly\n\nShip fast, Quality Follows\n\nObsess over customers\n\nBe a part of this exciting journey with us!\nAbout the Role\nOpusClip is looking for a staff-level, product-oriented Data Science leader to build an effective and increasingly AI-native data function.\nYou 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.\nThis 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.\nYou 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.\nThis expands the existing role from owning trusted metrics and analyses into setting direction and creating leverage across the Data function.\nWhat You’ll Do\nLead the Data function\nSet priorities for a small Data team and focus limited capacity on the highest-impact problems.\n\nPersonally lead ambiguous or high-stakes analytical projects.\n\nRaise standards for metrics, experimentation, analytical quality, and decision-making.\n\nLead and develop Data Scientists, analysts, and Data Engineers through technical direction and example.\n\nReduce repetitive and reactive work by turning recurring problems into reusable systems and processes.\n\nDrive product and business decisions\nAnalyze activation, retention, segmentation, monetization, user behavior, and lifetime value.\n\nTranslate ambiguous business questions into rigorous analysis and clear recommendations.\n\nIdentify opportunities where Data can directly improve key company metrics.\n\nBuild stronger user profiling and segmentation to inform product strategy, operations, and company goal setting.\n\nImprove experimentation and causal measurement across Product and Growth.\n\nThe existing JD already emphasizes turning product and customer data into business decisions; this role owns that mandate at a broader level.\nImprove data quality and measurement\nEstablish trusted definitions and validation for critical product and business metrics.\n\nIdentify systematic issues across tracking, pipelines, transformations, tables, and dashboards.\n\nPartner with Data Engineering and Engineering to prevent recurring data problems rather than repeatedly fixing symptoms.\n\nBuild reusable datasets, metric definitions, monitoring, and analytical frameworks that improve self-service.\n\nYou do not need to be a data infrastructure expert, but you should be technically strong enough to diagnose how data moves through a system, identify systemic failure modes, and work effectively with engineers to fix them.\nBuild Growth intelligence\nHelp Growth understand acquisition quality, retention, LTV, and the true value of different channels and customer segments.\n\nImprove performance marketing measurement beyond surface-level attribution toward experimentation and incrementality.\n\nIdentify opportunities to improve CAC, conversion, retention, monetization, or other major business metrics.\n\nBuild horizontal analytical tools and frameworks that enable Growth and Product teams to run better experiments and make faster decisions.\n\nPartner with our AI teams\nSupport data curation, evaluation design, experimentation, and measurement for AI-powered product experiences.\n\nIdentify product behavior that can become useful evaluation data, feedback signals, or failure cases.\n\nConnect AI quality with real user behavior and business outcomes.\n\nStrengthen the loop from product usage → data → AI improvement → better product.\n\nModel training experience is not required. The existing role already includes AI evaluation, curation, and online/offline measurement; this senior role is expected to make that collaboration systematic.\nBuild AI-native analytics\nUse AI to automate recurring analytical work and improve the productivity of the Data team.\n\nBuild trusted self-service tools for Product and business teams.\n\nExplore agentic systems that can detect unusual metric movements, identify contributing segments, generate hypotheses, and investigate likely causes.\n\nHelp move the company from dashboards and one-off analysis toward proactive business intelligence.\n\nWhat We’re Looking For\nSignificant experience in data science, product analytics, decision science, or a closely related field.\n\nDemonstrated Staff, Principal, Lead, or equivalent scope, regardless of formal title.\n\nStrong product and business judgment. You identify important questions instead of waiting for them to be assigned.\n\nStrong SQL and Python skills and a willingness to remain hands-on.\n\nDeep experience with product metrics, retention, segmentation, monetization, or experimentation.\n\nStrong understanding of statistics, A/B testing, and causal reasoning.\n\nStrong data-quality instincts and enough data-engineering knowledge to diagnose systemic pipeline problems.\n\nAbility to turn one-off analyses into reusable tools, frameworks, datasets, or processes.\n\nAbility to lead through influence, technical credibility, and clear communication.\n\nStrong ownership and effectiveness in ambiguous environments.\n\nNice to Have\nGrowth analytics, incrementality, LTV, attribution, or causal inference experience.\n\nExperience working with AI/ML teams on evaluation or data curation.\n\nExperience building AI-assisted or agentic analytics systems.\n\nData engineering experience with pipelines, transformations, backfills, or automated validation.\n\nExperience building user segmentation or behavioral profiling systems.\n\nExperience in SaaS, consumer software, creator products, subscription businesses, or AI products.\n\nFamiliarity with BigQuery, Mixpanel, Statsig, Superset, Prefect, Airflow, dbt, or similar tools.\n\nDeep data infrastructure expertise and model-training experience are not required.\nWhat Success Looks Like\nGood success - Build an effective Data Science function for an AI product\nWithin your first 6-12 months, you will have:\nSystematically improved our data practices across metric quality, experimentation, data validation, and analytical workflows.\n\nDriven 1-2 high-impact projects where Data contributes roughly 8%+ improvement to an important business metric such as retention, conversion, CAC, monetization, or product adoption.\n\nBuilt horizontal tools and frameworks that help multiple Product, Growth, or AI teams deliver better outcomes without depending on repeated one-off analysis.\n\nFunctionally led a small Data group of approximately 1 Data Scientist, 2 analysts, and 1-2 Data Engineers, raising the quality and focus of the team regardless of formal reporting structure.\n\nEstablished an effective data flywheel with our AI teams, turning product behavior into better evaluation data, feedback signals, and measurable product improvements.\n\nWildly successful - Turn the tide\nExceptional performance means using Data to materially change the trajectory of the company.\nExamples include:\nDiscovering insights or mechanisms that enable a path toward 2× growth, such as a major improvement in retention, a substantial reduction in CAC, or a new source of monetization or product growth.\n\nIdentifying a repeatable scaling law or growth mechanism that creates a portfolio of high-value projects capable of productively engaging roughly 30% of the company for 12+ months.\n\nTurning Data from a support function into a source of company strategy, consistently identifying important opportunities that Product, Growth, or AI teams would not otherwise have discovered.\n\nWhy Join Us\nShape the Data function. You will have significant freedom to define how a modern Data team should operate.\n\nHave direct business impact. Your work can influence product direction, Growth investment, monetization, and AI product quality.\n\nBuild AI-native data systems. Go beyond dashboards toward self-service, proactive insights, and agentic analytics.\n\nStay hands-on. Seniority here does not mean giving up difficult analytical and technical work.\n\nBuild the AI data flywheel. Turn real product behavior into better evaluation, measurement, and product improvement.\n\nBring a relatively modern stack including BigQuery, Mixpanel, Superset, Python, Prefect, and Statsig to the next level.\n\nEEO\nOpusClip 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. 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