Senior Data Scientist - Consumer Marketing

Microsoft Microsoft · Big Tech · Redmond, WA +3 · Data Science

Senior Data Scientist role focused on consumer marketing personalization and decision science. Responsibilities include defining technical strategy for causal modeling and experimentation, architecting production-grade personalization models, leading ML efforts from framing to deployment, and guiding LLM engineering direction. The role emphasizes collaboration, technical leadership, and driving measurable business outcomes.

What you'd actually do

  1. Define and lead the technical strategy for causal modeling & virtual experimentation approaches (simulation, synthetic controls/data generation where appropriate) to de-risk decisions and accelerate learning.
  2. Collaborate tightly with internal and external partners on the technical strategy for user segmentation at scale, combining clickstream, CRM (customer relationship management), product telemetry, and campaign data to power personalized experiences.
  3. Architect and deliver production-grade personalization models for website and outbound channels (email, push, lifecycle, campaign orchestration).
  4. Lead complex ML (machine learning) efforts from problem framing to deployment, monitoring, drift detection, retraining strategy, and business readouts.
  5. Partner deeply with SME’s (subject matter experts) and cross-functional teams to guide LLM (large language model) engineering direction, including model selection, evaluation frameworks, prompt/system design, grounding patterns, and responsible deployment practices.

Skills

Required

  • Python
  • SQL
  • large-scale data processing
  • feature engineering
  • segmentation systems
  • personalization systems
  • large behavioral datasets
  • experimentation
  • causal inference
  • statistical decision frameworks
  • cross-functional technical initiatives
  • executive-level recommendations
  • LLM engineering practices
  • eval harnesses
  • RAG/grounding patterns
  • prompt workflows
  • model operations
  • synthetic experimentation methods
  • simulation-based design
  • responsible AI
  • model risk management
  • governance in enterprise environments

Nice to have

  • real-time or near-real-time personalization architectures
  • synthetic experimentation methods
  • simulation-based design
  • responsible AI
  • model risk management
  • governance in enterprise environments
  • creating reusable platforms/assets that improve organizational velocity

What the JD emphasized

  • production impact
  • production-grade personalization models
  • LLM engineering direction
  • responsible deployment practices
  • real-time or near-real-time personalization architectures
  • responsible AI, model risk management, and governance

Other signals

  • personalization models
  • LLM engineering
  • experimentation
  • causal modeling