Senior Applied Scientist - AI for Safe Changes Health Gating

Overview We 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.

Responsibilities

Scientific 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.

Required 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 Francis