Role Overview

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.

Key Responsibilities

Marketing Measurement & Causal Inference

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

Required

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

Preferred

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

What We Offer

Fully remote work environment

Competitive salary and equity opportunities

Performance bonuses

Health, dental, and vision benefits

Other benefits such as daily lunch stipend, monthly wifi, cell phone and subscription reimbursement, and annual hardware stipend

Flexible PTO and remote-friendly culture

Bi-annual Corporate Offsites

Opportunity to help shape a function at a rapidly scaling tech company