Principal Applied Scientist

Microsoft

Hybrid

Quick summary

Work type
Hybrid
Location
Salary
$142,800–$274,800 / yr
Posted
32 days ago
Closes
Nov 22, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $198k
This role $209k
$127k most similar roles pay here $291k

This role pays more than 59% of similar roles. Most pay $162,000–$234,150 — the shaded band above. At the midpoint, this role pays about $209k versus about $198k for comparable roles.

Based on 240 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 622 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 571 roles with salary data.

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At a glance

TL;DR · Principal Applied Scientist

As a Principal Applied Scientist on the Signals Modeling team, you will lead the development of advanced data-driven attribution and causal measurement methodologies that power our advertising marketplace. Your day-to-day involves defining technical strategies for large-scale learning systems, establishing rigorous evaluation standards, and introducing cutting-edge inference techniques into production environments. You will work closely with research, engineering, and product teams to enhance bidding, ranking, and optimization algorithms at web scale, ensuring accurate attribution and measurement that influences billions in advertising spend. The role requires deep expertise in causal inference, counterfactual learning, and experimental design, as well as experience leading high-impact initiatives where methodological rigor is crucial. Proficiency in machine learning frameworks, strong scientific judgment, and the ability to communicate complex technical concepts are essential, alongside a track record of driving measurable business impact through advanced modeling approaches.

What you'll do

  • Define and drive the scientific and technical strategy for data-driven attribution and causal measurement in advertising systems.
  • Establish methodologies for incrementality estimation, counterfactual learning, delayed-feedback modeling, and bias correction in partially observable environments.
  • Lead design and production adoption of frameworks improving bidding, ranking, optimization, and advertiser ROI at web scale.
  • Set evaluation standards distinguishing correlation from causation to elevate experimental rigor across teams.
  • Identify capability gaps and introduce advanced research or modeling approaches to strengthen measurement foundations.
  • Serve as a subject-matter expert and technical advisor on attribution and causal inference methodologies.

What we're looking for

  • Master's degree in a relevant field (Statistics, Computer Science, etc.) with 4+ years of related experience.
  • Deep expertise in causal inference, data-driven attribution, and experimental design applied in production environments.
  • Proven ability to lead high-impact initiatives with limited ground truth and critical methodological rigor.
  • Significant experience developing and deploying large-scale machine learning systems across product lifecycles.
  • Exceptional communication skills for translating complex technical concepts to senior leaders.
  • Recognized expertise in attribution, incrementality, marketplace experimentation, or causal ML.
  • Track record of driving multi-year research agendas that improved product outcomes significantly.

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