The Signals Modeling team develops large-scale learning systems for advertising marketplace optimization, focusing on user behavior, impact measurement, and outcome optimization. The Principal Applied Scientist will lead data-driven attribution and causal measurement strategies, develop methodologies for incrementality estimation, counterfactual learning, and bias correction, and drive adoption of attribution frameworks to improve bidding, ranking, and advertiser ROI. The role requires expertise in causal inference, experimental design, and production ML systems, with responsibilities including mentoring scientists and influencing technical strategy. Candidates must have advanced degrees in relevant fields with significant experience in statistics, econometrics, or computer science, and a proven track record in leading large-scale machine learning or statistical systems. The position is full-time, on-site in Redmond, WA or Sunnyvale, CA, with salary ranges from $142,800 to $304,200 depending on location.
What you'll do
Define and drive scientific and technical strategy for data-driven attribution and causal measurement across advertising systems
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Establish methodologies for incrementality estimation, counterfactual learning, delayed-feedback modeling, and bias correction
Lead design and production adoption of attribution and causal inference frameworks to improve bidding, ranking, optimization, and advertiser ROI
Set evaluation standards to distinguish correlation from causation and elevate experimental rigor
Identify capability gaps and introduce advanced research, tools, or modeling approaches
Operate across organizational boundaries to align research, engineering, product, and business leaders on measurement strategy
Serve as subject-matter expert and technical advisor on attribution and causal inference
Mentor scientists and influence technical direction to raise scientific bar
Requirements
Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience
OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience
OR equivalent experience
Preferred: Master's Degree AND 9+ years related experience or Doctorate AND 6+ years related experience or equivalent
Demonstrated track record of setting technical direction for large-scale machine learning or statistical systems
Deep expertise in causal inference, data-driven attribution, treatment effect estimation, counterfactual learning, or experimental design in production
Experience leading ambiguous, high-impact initiatives with limited ground truth and methodological rigor
Proven ability to influence strategy and drive adoption of new measurement or modeling approaches
Significant experience developing and deploying production ML systems across product lifecycle
Solid scientific judgment selecting appropriate methodologies under real-world constraints
Exceptional communication skills for technical and business leaders
Recognized expertise in attribution, incrementality, marketplace experimentation, or causal ML
Track record of driving multi-year research or modeling agendas improving product outcomes
Experience defining measurement strategy for advertising platforms, marketplaces, or recommendation systems
Publications, patents, or widely adopted internal methodologies in causal inference, experimentation, econometrics, or applied ML
History of mentoring senior scientists and elevating organizational scientific capability
Experience influencing director- or VP-level technical strategy
Certain roles may be eligible for benefits and other compensationLink to additional benefits and pay information: https://careers.microsoft.com/us/en/us-corporate-pay
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