Deliver ML-powered audience solutions that drive advertiser outcomes
Design, build, and deploy high-impact propensity and lifetime value (LTV) models that meaningfully improve targeting effectiveness, ROAS, and campaign performance for top advertisers (e.g., P&G, PepsiCo)
Own technical partnerships with strategic advertisers
Act as the primary data science partner for large advertisers and measurement partners
Scale models from concept to production
Lead the end-to-end lifecycle of ML solutions — moving from POC to production
Advance experimentation for onsite display media
Design and analyze rigorous A/B tests to evaluate onsite display ad strategies
Automate insights through GenAI-powered storytelling
Influence executive decision-making through storytelling

Requirements

Proven experience building ML models that drive real business impact
3+ years of hands-on, non-academic experience developing and deploying ML models end-to-end — particularly propensity and/or LTV models using tree-based methods (XGBoost, LightGBM)
Strong experimentation and statistical judgment
Deep expertise in A/B testing design and analysis, including power analysis, hypothesis testing, and advanced methods (bootstrapping or Bayesian approaches are a plus)
Experience operationalizing analytics at scale
Comfort turning analytical ideas into production-ready solutions using Python and SQL
GenAI and automation mindset
Demonstrated ability to leverage LLMs or GenAI tools to automate narrative generation, reporting, or presentation creation from data.
Ability to bridge data science and executive storytelling
A track record of communicating complex analytical results to senior business stakeholders in a clear, persuasive, and strategic way
Retail media or AdTech exposure (strong plus)
Familiarity with Retail Media Networks, AdTech platforms, or closed-loop measurement environments is highly valued
Modern data & ML tooling experience
Experience with GCP (BigQuery, Dataproc, Vertex AI preferred), and familiarity with engineering best practices such as CI/CD, Docker, PySpark, or scalable data pipelines
Strong quantitative foundation
A Master’s degree or PhD in Statistics, Economics, Computer Science, Mathematics, or a related field is preferred

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningpropensity modelslifetime value modelsA/B testingstatistical analysisPythonSQLXGBoostLightGBMdata automation

Soft Skills

storytellingcommunicationinfluencecollaborationanalytical thinkingstrategic thinkingproblem-solvingleadershipdecision-makingcreativity

Certifications

Master’s degreePhD in StatisticsPhD in EconomicsPhD in Computer SciencePhD in Mathematics