Remote Data Scientist, Marketing Measurement & Causal Inference

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

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.