Data Scientist

Role OverviewThe Data Scientist will own the measurement science behind Audiohook's performance audio advertising platform. You'll design and run incrementality tests, build and maintain marketing mix models, and apply causal analysis to quantify how Audiohook drives outcomes for advertisers. This role combines hands-on modeling with the opportunity to shape how we prove value to customers, sharpen our bidding and optimization systems, and influence product direction. You'll collaborate closely with Engineering, Product, Sales, and Customer Success to ensure measurement isn't just statistically sound but operationally useful.Key ResponsibilitiesMarketing Measurement & Causal InferenceDesign and run incrementality experiments (geo, ghost bidding, holdout, PSA) that quantify Audiohook's lift for advertisersBuild, maintain, and evolve marketing mix models (MMM) and multi-touch attribution analyses across customer campaignsApply causal inference methods — difference-in-differences, synthetic controls, instrumental variables, propensity scoring — to questions that can't be answered with RCTsTranslate measurement results into clear narratives for advertisers, internal stakeholders, and the product teamModeling & AnalysisPartner with Engineering on the data and modeling layer that powers bidding, pacing, and optimization decisionsDevelop and validate predictive models that improve campaign performance and platform efficiencyInstrument experiments and analyses for reproducibility, monitoring, and ongoing measurement qualityCross-Functional CollaborationPartner with Sales and Customer Success on measurement studies for priority accounts and renewalsPartner with Product on roadmap inputs grounded in causal evidence, not just descriptive dataPresent findings to advertisers, internal teams, and leadership in clear, decision-ready formatsCommunicate clearly and proactively in a remote-first environmentQualificationsRequiredBachelor's or Master's degree in Statistics, Economics, Data Science, Computer Science, or related quantitative field3–5 years of applied data science experience with a focus on marketing measurement — incrementality, MMM, attribution, or causal analysisHands-on experience designing and analyzing experiments (A/B, geo, holdout) in a marketing or advertising contextStrong fluency in Python (pandas, statsmodels, scikit-learn, PyMC, or similar) and SQLSolid grounding in statistical inference, regression, and causal methodsAbility to communicate technical results to non-technical audiences — advertisers, sales, leadershipExcellent attention to detail and intellectual honesty about model limitationsPreferredExperience in adtech, digital advertising, or media measurementExperience with Bayesian methods or Bayesian MMM frameworks (e.g., PyMC-Marketing, LightweightMMM, Robyn)Experience working with large-scale ad event data (impressions, clicks, conversions) and modern data stacks (e.g., Iceberg, Snowflake, BigQuery)Experience in a startup or high-growth companyComfort using AI tools to accelerate exploratory analysis, code, and write-ups while maintaining methodological rigorWhat We OfferFully remote work environmentCompetitive salary and equity opportunitiesPerformance bonusesHealth, dental, and vision benefitsOther benefits such as daily lunch stipend, monthly wifi, cell phone and subscription reimbursement, and annual hardware stipendFlexible PTO and remote-friendly cultureBi-annual Corporate OffsitesOpportunity to help shape a function at a rapidly scaling tech companyJ-18808-Ljbffr