Principal Applied Scientist
Core
Define and drive scientific and technical strategy for data-driven attribution (DDA) and causal measurement across advertising systems to improve bidding, ranking, and advertiser ROI.
Role type
Principal Applied Scientist (Causal Inference & Attribution)
Builds
Attribution and causal inference frameworks for web-scale advertising systems
Domain
Advertising technology / Causal inference / Machine Learning
Deliverable
production ML models
Required skills
Causal inference, data-driven attribution, treatment effect estimation, counterfactual learning, experimental design, bias correction, delayed-feedback modeling, statistical strategy, production ML system deployment, organizational alignment, technical mentorship (via careerplan.io/jobs/1970393556869617-principal-applied-scientist-at-microsoft)
Preferred skills
Marketplace experimentation, multi-year research agenda definition, measurement strategy for large-scale recommendation systems, publications/patents in causal ML, influencing director/VP-level strategy
Technologies
N/A
Responsibilities
Establish methodologies for incrementality estimation and counterfactual learning; Lead design and production adoption of attribution frameworks; Set evaluation standards distinguishing correlation from causation; Identify capability gaps and introduce advanced modeling approaches; Operate across organizational boundaries to align research, engineering, product, and business leaders; Mentor scientists and influence technical direction
Seniority
Principal, strategy & mentorship
