Principal Applied Scientist

Microsoft•Redmond, WA

10d•Hybrid

About The Position

Our Signals Modeling team builds the intelligence that powers how the advertising marketplace understands user behavior, measures impact and optimizes outcomes from initial impressions through downstream conversions and long-term advertiser value. We develop large-scale learning systems that infer intent and causal effects from incomplete and noisy feedback, enabling principled decision-making across ranking, bidding, pricing, and budget allocation. Our work sits at the foundation of marketplace optimization, where accurate attribution and measurement directly influence billions in advertising spend. The team designs and operates state-of-the-art modeling platforms spanning representation learning, weak-supervision, multi-objective training, calibration, and rigorous experimentation. We transform sparse engagement signals into reliable learning targets and build models that remain robust under delayed conversions, selection bias, and rapidly shifting marketplace dynamics. As a Principal Applied Scientist, you will help define the future of data-driven attribution and causal measurement, shaping the methodologies that determine how value is estimated and optimized across the ecosystem. You will partner across research, engineering, and product leadership to introduce advanced inference techniques into production systems operating at massive scale. This is a high-ownership role focused on solving structurally hard problems where ground truth is limited, experimentation is non-trivial, and scientific rigor is essential to unlocking durable marketplace advantage. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
OR equivalent experience.

Nice To Haves

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
OR equivalent experience.
5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
5+ years experience conducting research as part of a research program (in academic or industry settings).
3+ years experience developing and deploying live production systems, as part of a product team.
3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
Recognized expertise in attribution, incrementality, marketplace experimentation, or causal ML.
Track record of driving multi-year research or modeling agendas that materially improved product outcomes.
Experience defining measurement strategy for advertising platforms, marketplaces, or large-scale recommendation systems.
Publications, patents, or widely adopted internal methodologies in causal inference, experimentation, econometrics, or applied machine learning.
History of mentoring senior scientists and elevating organizational scientific capability.
Experience influencing director- or VP-level technical strategy.
Demonstrated track record of setting technical direction for large-scale machine learning or statistical systems that delivered measurable business impact.
Deep expertise in causal inference, data-driven attribution, treatment effect estimation, counterfactual learning, or experimental design — applied in production environments.
Experience leading ambiguous, high-impact initiatives where ground truth is limited and methodological rigor is critical.
Proven ability to influence strategy and drive adoption of new measurement or modeling approaches beyond an immediate team.
Significant experience developing and deploying production ML systems across multiple stages of the product lifecycle.
Solid scientific judgment with a history of selecting appropriate methodologies under real-world constraints.
Exceptional communication skills with the ability to translate complex technical concepts into guidance for senior technical and business leaders.

Responsibilities

Define and drive the scientific and technical strategy for data-driven attribution (DDA) and causal measurement across advertising systems.
Establish methodologies for incrementality estimation, counterfactual learning, delayed-feedback modeling, and bias correction in environments with partial observability.
Lead the design and production adoption of attribution and causal inference frameworks that improve bidding, ranking, optimization, and advertiser ROI at web scale.
Set evaluation standards that distinguish correlation from causation and elevate experimental rigor across teams.
Identify capability gaps and introduce advanced research, tools, or modeling approaches to strengthen measurement foundations.
Operate across organizational boundaries to align research, engineering, product, and business leaders on measurement strategy.
Serve as a subject-matter expert and technical advisor on attribution and causal inference.
Mentor scientists and influence technical direction to raise the organization’s scientific bar.

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