Data Scientist

Audiohook; United States; Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Fully remote work

Equity opportunities

Health, dental, and vision benefits

Bi-annual corporate offsites

Job summary

Audiohook seeks a data scientist to lead measurement for its performance audio advertising platform, designing incrementality experiments and evolving marketing mix models while influencing bidding systems, optimization, and product direction.

You will partner with engineering on data and modeling layers for campaign performance, apply causal inference techniques, and translate findings into decision-ready narratives for advertisers and internal teams.

Qualifications

• Bachelor’s or Master’s degree in a quantitative field.
• 3–5 years of applied data science experience in marketing measurement.
• Experience designing and analyzing experiments (A/B tests, geo tests, holdouts).
• Strong Python and SQL with solid statistical inference and causal methods.
• Ability to communicate results clearly to non-technical audiences.

Responsibilities

• Design and execute incrementality experiments (geo tests, holdouts, PSA studies).
• Build, maintain, and refine marketing mix models and multi-touch attribution analyses.
• Apply causal inference methods where randomized trials aren’t feasible.
• Partner with engineering on data/modeling that drives bidding, pacing, and optimization.
• Instrument experiments for reproducibility, monitoring, and measurement quality.
• Translate findings into narratives for advertisers, sales, and product teams.

Skills

Python

SQL

Statistical inference

Causal analysis

Communication

Education

Bachelor's or Master's degree in a quantitative field

Tools

PyMC

Robyn

LightweightMMM

Snowflake

BigQuery

Iceberg

Job description

Role overview

A data scientist is needed to lead the measurement function for a performance audio advertising platform. The role centers on designing incrementality experiments, building and evolving marketing mix models, and applying causal inference to demonstrate the platform's value to advertisers. It blends hands‑on modeling with cross-functional influence on bidding systems, optimization, and product direction.

Responsibilities

• Design and execute incrementality experiments such as geo tests, ghost bidding, holdouts, and PSA studies to quantify advertiser lift.
• Build, maintain, and refine marketing mix models and multi-touch attribution analyses across customer campaigns.
• Apply causal inference techniques including difference‑in‑differences, synthetic controls, instrumental variables, and propensity scoring where randomized trials are not feasible.
• Partner with engineering on the data and modeling layer that drives bidding, pacing, and optimization decisions, including predictive models for campaign performance.
• Instrument experiments and analyses for reproducibility, monitoring, and ongoing measurement quality.
• Translate findings into clear narratives for advertisers, internal stakeholders, sales, customer success, and product teams, presenting results in decision‑ready formats.

Requirements

• Bachelor's or Master's degree in Statistics, Economics, Data Science, Computer Science, or a related quantitative field.
• Three to five years of applied data science experience with a focus on marketing measurement, including incrementality, MMM, attribution, or causal analysis.
• Hands‑on experience designing and analyzing experiments such as A/B tests, geo tests, or holdouts in a marketing or advertising context.
• Strong proficiency in Python (pandas, statsmodels, scikit‑learn, PyMC, or similar) and SQL.
• Solid grounding in statistical inference, regression, and causal methods, with attention to detail and intellectual honesty about model limitations.
• Ability to communicate technical results clearly to non‑technical audiences including advertisers, sales, and leadership.

Nice to have

• Background in adtech, digital advertising, or media measurement.
• Experience with Bayesian methods or Bayesian MMM frameworks such as PyMC‑Marketing, LightweightMMM, or Robyn.
• Familiarity with large‑scale ad event data (impressions, clicks, conversions) and modern data stacks like Iceberg, Snowflake, or BigQuery.
• Experience working in a startup or high‑growth company.
• Comfort using AI tools to accelerate exploratory analysis and documentation while preserving methodological rigor.

Benefits and work setup

• Fully remote work environment with flexible PTO and a remote‑friendly culture.
• Competitive salary with equity opportunities and performance bonuses.
• Health, dental, and vision benefits.
• Daily lunch stipend, plus monthly reimbursements for wifi, cell phone, and subscriptions, and an annual hardware stipend.
• Bi‑annual corporate offsites.