Marketing Data Science Manager

Marketing Data Science Manager Location: RemoteDuration: Long TermSkills:Domain Background and Communication:Proven ability to present technical insights to senior marketing stakeholdersStrong business storytelling and decision-making influenceSolid grasp of media channels and marketing strategyOptimization model and analysis:Experience with search/tROAS models/TV creative optimization or similar optimization workAbility to translate MMM outputs into ROI-based spend recommendationsEnd-to-end MMM build experience (not just maintenance)Strong understanding of adstock, saturation, lag effects, and model validationHands on and programming:5+ years hands-on modeling in Python/R + strong SQLExperience with AWS (SageMaker, EC2, Lambda) for model deployment will be a plusRoles and Responsibilities:Core Technical Expertise (Non-negotiable) Causal & Predictive Analytics:Strong in lift measurement, incrementality, and experimental design.Able to frame business problems using appropriate methods (e.g., Bayesian, regression, uplift modeling, time-series forecasting, optimization, casual ML).Ability to quantify uncertainty and defend ROI confidence intervals.Strong experimentation expertise: geo tests, lift tests, synthetic controls, DiD.Marketing Mix Modeling (MMM):Has built MMM end-to-end and have deep understanding on marketing media optimization, not just contributed.Experience scaling MMM across multiple regions/products.Experience presenting MMM outputs to executives and adjusting the model based on business feedback.Search / tROAS / Value-Based Bidding:Hands-on experience building or maintaining:tROAS models, geo experiments, incrementality testsMedia spend optimizers and bid strategiesUnderstands channel behavior and measurement nuances.Business & Stakeholder Communication:The candidate must:Communicate complex modeling clearly to VP-level, non-technical audiences.Translate model outputs into actionable marketing recommendations (e.g., where to shift $20M).Defend model assumptions and ROI estimates under pressure.Demonstrate ownership of cross-functional stakeholder conversations.This is where many technical candidates fail. We need someone who thinks like a marketing strategist, not only a modeler.Marketing Domain Knowledge:Candidate must understand:Media channels (search, display, paid social, TV, OLV, direct mail)Funnel stages, attribution issues, incrementalityBudget allocation logic and marginal ROI curvesCreative impact and measurement This cannot be taught easily—must have hands-on applied experience.Hands-On ML + AWS/MLOps (Good to have):5+ years Python/R/STATS modelingStrong with SQL and large data setsDeployment experience using:AWS SageMaker, Lambda, EC2Terraform (nice to have)Comfortable owning production pipelines (MMM, optimizers, segmentation, etc.)Leadership & Ownership:Can lead projects end-to-end with minimal direction.Can guide analysts or engineers and review their modeling work.Comfortable setting standards for MMM, creative measurement, and search models.Can influence marketing, product, and finance partners.Ideal Background:10 years marketing analytics and data scienceExperience at:Education, Retail, e-commerce, telecom, CPG, or B2C at scaleMedia agencies with MMM ownership (e.g., Analytic Partners, Neustar, Gain Theory, Merkle)Companies with strong internal MMM teamsPrior role titles:Senior/Lead Marketing Data ScientistMarketing Science ManagerEconometrics LeadMedia Measurement Lead