Data Scientist

Audiohook•Eden, UT

10d•$120,000 - $170,000•Remote

About The Position

The 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.

Requirements

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
Ability to communicate technical results to non-technical audiences — advertisers, sales, leadership
Excellent attention to detail and intellectual honesty about model limitations

Nice To Haves

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

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
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
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