Data Scientist, Marketing Analytics

This role will partner closely with external data vendors and media partners, lead geo-experimentation 
design and measurement, and develop propensity and optimization models that guide media investment
decisions. We value continuous learning, rigorous causal thinking, and the ability to translate complex 
analysis into clear, actionable recommendations for marketing leaders.

• Marketing Mix Modeling: Serve as the internal analytical counterpart to our Marketing Mix 
  Modeling vendor, reviewing quarterly model refreshes for accuracy, geographic granularity, 
  variable specification, and channel decay assumptions, and reconciling MMM outputs against 
  internal experimental results. Build MMM in-house to measure the return on investment (ROI) 
  across different advertising channels. Translate MMM and incrementality outputs into 
  media/channel budget allocation recommendations, working with internal stakeholders to connect 
  model output to planning cycles
• Experimentation & Measurement: Design and analyze geo-experiments, media heavy-up/heavy-down designs, and matched-market or synthetic control tests across channels, 
  including market selection, statistical power/MDE calculations, and exclusion protocols for 
  confounding risk e.g., seasonal exposure, outlet share, promotions, etc.
• Build Optimization Frameworks: Design randomized holdout structures to measure incremental 
  value of Email/SMS programs, and develop send-time, frequency, and content optimization 
  models in partnership with other marketing entities
• Develop Propensity & Customer Models: Build propensity-to-purchase, churn, and channel-response models using transactional, loyalty, and behavioral data to support targeting, 
  personalization, and customer segmentation
• Analyze Trends & Validate Data: Interpret customer and media performance data for actionable 
  patterns, assess new data sources for improving model(s) accuracy, and maintain rigorous data 
  validation standards
• Collaborate for Implementation: Partner with internal teams in advanced analytics work to 
  operationalize models and ensure measurement infrastructure scales
   

• Experience: 3–5 years in data science, marketing analytics, or applied statistics, ideally with 
  direct exposure to marketing measurement in a retail, or omnichannel environment
• Technical Skills: Proficiency in Python and/or R and SQL; hands-on experience with causal 
  inference methods, Bayesian modeling, experimental design and power analysis, and marketing 
  mix modeling concepts. Familiarity with Excel, cloud data warehouses and BI/visualization tools 
  (Tableau, Looker, GCP, CDP)
• Dynamic Environment: Comfort working in a research-oriented group with multiple concurrent 
  projects
• Communicate for Impact: Ability to translate statistical and modeling work into clear, decisionready recommendations for non-technical executive stakeholders
• Educational Background: Bachelor's degree in statistics, information systems, data science, 
  applied mathematics, economics, computer science, or related discipline; Master's degree 
  preferred