Data Scientist

Key Responsibilities

• Design and run incrementality experiments (geo, ghost bidding, holdout, PSA) that quantify Audiohook's lift for advertisers
• Build, maintain, and evolve marketing mix models (MMM) and multi-touch attribution analyses across customer campaigns
• Apply causal inference methods — difference-in-differences, synthetic controls, instrumental variables, propensity scoring — to questions that can’t be answered with RCTs
• Translate measurement results into clear narratives for advertisers, internal stakeholders, and the product team
• Modeling & Analysis: Partner with Engineering on the data and modeling layer that powers bidding, pacing, and optimization decisions
• Develop and validate predictive models that improve campaign performance and platform efficiency
• Instrument experiments and analyses for reproducibility, monitoring, and ongoing measurement quality
• Cross-Functional Collaboration: Partner with Sales and Customer Success on measurement studies for priority accounts and renewals
• Partner with Product on roadmap inputs grounded in causal evidence, not just descriptive data
• Present findings to advertisers, internal teams, and leadership in clear, decision-ready formats
• Communicate clearly and proactively in a remote-first environment

Qualifications

• Bachelor's or Master's degree in Statistics, Economics, Data Science, Computer Science, or related quantitative field
• 3–5 years of applied data science experience with a focus on marketing measurement — incrementality, MMM, attribution, or causal analysis
• Hands-on experience designing and analyzing experiments (A/B, geo, holdout) in a marketing or advertising context
• Strong fluency in Python (pandas, statsmodels, scikit-learn, PyMC, or similar) and SQL
• Solid grounding in statistical inference, regression, and causal methods
• Able to communicate technical results to non-technical audiences — advertisers, sales, leadership
• Excellent attention to detail and intellectual honesty about model limitations

Preferred Experience

• Experience in adtech, digital advertising, or media measurement
• Experience 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 company
• Comfort using AI tools to accelerate exploratory analysis, code, and write-ups while maintaining methodological rigor