Data Scientist
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Before you apply
LocationUnited States
Work moderemote
Typefull-time
SeniorityMid
Experience3–5 yrs
DepartmentEngineering
EquityIncluded
Company size51–200 people
PostedSep 15, 2026 · 1mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
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
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
Skills
Python
pandas
statsmodels
scikit-learn
PyMC
SQL
PyMC-Marketing
LightweightMMM
Robyn
Iceberg
Snowflake
BigQuery
Benefits
Health insurance
Dental insurance
Vision insurance
Remote stipend
PTO
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
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Partner with Engineering on the data and modeling layer that powers bidding, pacing, and optimization decisions
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Develop and validate predictive models that improve campaign performance and platform efficiency
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Instrument experiments and analyses for reproducibility, monitoring, and ongoing measurement quality
Cross-Functional Collaboration
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Partner with Sales and Customer Success on measurement studies for priority accounts and renewals
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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