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. Qualifications 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, librari