OverviewWe are seeking an Applied Scientist to develop machine-learning and AI solutions that improve the safety, reliability, and operational efficiency of Microsoft 365 services, including systems supporting Microsoft 365 Copilot.
You will focus on scenario health gating: using service telemetry, change signals, experiments, and operational data to detect regressions, estimate risk, and inform automated or human-reviewed production decisions. You will translate ambiguous reliability problems into measurable scientific questions, develop and evaluate algorithms, implement production-oriented solutions, and partner with software engineers and product managers to deploy and improve them in live environments.
The ideal candidate combines strong statistical and machine-learning foundations with practical engineering judgment. They are comfortable working with noisy, incomplete, and imbalanced data; reasoning with uncertainty and asymmetric risk; and communicating evidence-based recommendations to technical and cross-functional audiences.
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.
ResponsibilitiesScientific problem formulation and algorithm development
- Translate service-health and change-safety problems into scientific objectives, hypotheses, datasets, baselines, and success metrics.
- Develop and evaluate machine-learning, statistical, causal, time-series, or agent-based methods for regression detection, risk estimation, failure attribution, and gating decisions.
- Compare technical alternatives and explain tradeoffs involving accuracy, interpretability, latency, scale, cost, reliability, and customer risk.
- Address noisy, sparse, delayed, imbalanced, and distribution-shifted production data.
- Assess relevant research and adapt promising methods to Microsoft 365 operational scenarios.
Experimentation and evaluation
- Build representative evaluation datasets from historical, synthetic, simulated, and production-derived data.
- Design rigorous offline and production evaluations, including baselines, ablation studies, robustness testing, error analysis, and expert or human evaluation where appropriate.
- Measure precision, recall, false-positive and false-negative rates, calibration, uncertainty, detection latency, and operational cost.
- Create reproducible experimentation and evaluation pipelines.
- Use production telemetry, incident outcomes, and user feedback to improve models and identify failure modes.
Research-to-production delivery
- Implement algorithms, data-processing components, and evaluation systems in clean, tested, maintainable code.
- Partner with engineers to integrate scientific components into scalable services and agent workflows.
- Contribute to design decisions involving performance, reliability, diagnosability, monitoring, privacy, security, compliance, and cost.
- Support staged deployment, monitoring, post-launch analysis, and iterative improvement.
- Build reusable tools, libraries, and evaluation methods for additional scenarios.
Collaboration and technical leadership
- Independently drive the applied-science workstream for one or more defined health-gating scenarios.
- Collaborate with engineers, PMs, data scientists, service owners, and partner scientists.
- Communicate methods, assumptions, limitations, results, uncertainty, and recommendations clearly.
- Influence technical decisions through experimentation and scientific judgment.
- Participate in design, code, model, and experiment reviews.
- Share reusable practices and support peer development through knowledge sharing and mentoring.
Responsible and measurable AI
- Identify and mitigate risks involving privacy, security, fairness, unsafe automation, data leakage, and inappropriate production actions.
- Design appropriate human oversight, fallback behavior, and safeguards for consequential decisions.
- Track model, system, and business measures such as detection quality, gating effectiveness, robustness, automation coverage, operational-toil reduction, and production adoption.
QualificationsRequired Qualifications:- Bachelor'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 Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
Other Requirements:Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred Qualifications:- Master'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 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.
- 3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
- Experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
- 3+ years experience conducting research as part of a research program (in academic or industry settings).
- 1+ year(s) experience developing and deploying live production systems, as part of a product team.
- 1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
- Experience formulating ambiguous real-world problems as machine-learning, statistical, optimization, or experimental problems.
- Experience implementing and evaluating ML or statistical algorithms
- Experience with experimental design, baselines, evaluation metrics, statistical reasoning, and error analysis.
- Experience working with noisy, missing, imbalanced, biased, or distribution-shifted datasets.
- Ability to communicate technical findings, limitations, and recommendations to cross-functional partners.
- Experience with anomaly detection, time-series analysis, causal inference, risk modeling, forecasting, ranking, optimization, or decision-making under uncertainty.
- Experience building or evaluating AI agents, language-model systems, tool-using workflows, or multi-agent systems and agentic loops.
- Experience developing evaluation datasets, synthetic data, simulations, golden sets, or human-evaluation processes.
- Experience with cloud telemetry, observability, CI/CD, incident management, deployment safety, or distributed systems.
- Contributions demonstrated through shipped systems, patents, publications, open-source work, or substantial internal technical impact.
- Familiarity with Responsible AI, privacy, security, and compliance requirements.
#CAP #M365Core
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
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This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about
requesting accommodations. 
