Data Scientist

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.