Requirements
- Hands-on applied science or data science background — you should be as comfortable building models and writing production SQL as you are leading a team
- Experience owning a measurement function including attribution, incrementality, ad effectiveness, or campaign measurement with a track record of building trusted, scalable measurement infrastructure
- Experience leading or overseeing a BI function, including data products, reporting infrastructure, and self-service analytics
- Deep expertise in product analytics and experimentation, including A/B and multivariate testing, incrementality and lift analysis, causal inference, funnel and cohort analysis, and KPI forecasting
- Strong statistical foundation with the ability to design, execute, and interpret complex experimental and causal results
- Advanced SQL skills and experience working with large, event-level datasets; fluency with modern analytics and data platforms (e.g., Databricks, BigQuery, Snowflake) and visualization tools (e.g., Tableau)
- Experience working in adtech, search advertising, or performance marketing is required with direct exposure to auction mechanics, ranking systems, bid optimization, or marketplace analytics strongly preferred
- Strong product and business intuition, with a track record of influencing product strategy and connecting insights to growth, efficiency, and P&L impact
- Demonstrated ability to lead and develop analytical teams while remaining deeply hands‑on
- Ability to clearly communicate complex insights to executive audiences
- Degree in a quantitative field (Statistics, Economics, Mathematics, Computer Science, or similar); advanced degree a plus
- 8–12+ years of experience in applied science, data science, product analytics, experimentation, and/or performance measurement
- Comfort operating in and improving messy data environments; you won’t walk into clean, well-structured data and should have experience diagnosing data quality issues and building toward reliable infrastructure
What the job involves
- The Director, Applied Science & Product Analytics is responsible for putting data at the center of great product and business decision-making at adMarketplace
- This is a highly hands‑on, technically deep role
- We are looking for someone who is as comfortable writing SQL and building causal inference frameworks as they are presenting findings to executive leadership
- This person will drive a culture of proactive insight generation where the team finds issues before they become problems and identifies optimization levers
- You will partner closely with Product, Engineering, and Commercial teams to ensure insights translate into measurable product outcomes
- Product analytics and performance diagnostics across the marketplace
- Deep‑dig investigations into auction outcomes, click & conversion funnels, supply quality, advertiser ROI, financial analysis, user behavior, and ranking logic
- Developing frameworks to surface hidden performance issues, inefficiencies, and optimization opportunities
- Identifying problems, surfacing opportunities, and influencing product direction — partnering closely with Product, Engineering, Design, FP&A, and Commercial leadership
- Product health measurement, opportunity discovery, and forecasting
- Executive‑facing insights that shape roadmap priorities and investment decisions
- Diagnosing performance issues across auctions, ranking and relevance, and conversion funnels
- Analyzing supply–demand dynamics and marketplace balance
- Evaluating performance by advertiser, query, geo, device, and vertical segments
- Translating complex data into clear, prioritized problem statements for product teams
- Inherit and improve a complex, unstructured data environment — diagnosing data quality issues, establishing standards, and building toward a clean, reliable foundation for measurement and decision‑making
- Experimentation, Causal Measurement & Applied Science:
- Own experimentation, causal inference, and incrementality frameworks to ensure decisions are grounded in validated impact
- Partner with Product, Engineering, and Data Engineering to design, run, and interpret experiments evaluating features, ranking changes, and marketplace policies
- Apply applied science methodologies including causal inference, propensity modeling, and quasi‑experimental design to marketplace and product problems
- Standardize measurement, metrics, and reporting across product teams
- Build scalable analytics and experimentation systems that deliver fast, trusted insights from complex event‑level data and directly inform roadmap and investment decisions
- Executive Storytelling & Decision Support:
- Prepare and deliver clear, compelling narratives that connect product behavior to business outcomes
- Inform product direction, marketplace policies, and pricing and monetization decisions with rigorous, data‑backed analysis
- Serve as the analytical voice in executive conversations, translating complex findings into prioritized, actionable recommendations
- Measurement & BI Ownership:
- Own advertiser and campaign measurement frameworks including attribution, incrementality, and ad effectiveness methodology, ensuring advertisers and internal stakeholders have trusted, rigorous measurement of performance
- Lead the BI function, including BI engineers, reporting infrastructure, and self‑service analytics capabilities across the organization
- Define and enforce data quality standards, measurement best practices, and metric governance across product and commercial teams
- Build and maintain a single source of truth for marketplace KPIs, ensuring consistent, trusted data products are available to Product, Engineering, FP&A, and Commercial leadership
- Own the measurement roadmap identifying gaps in how performance is measured and systematically closing them
- Partner with Data Engineering to ensure the underlying data infrastructure supports fast, reliable, and scalable measurement and reporting