Senior Data Scientist

Microsoft•Redmond, WA

15h•Hybrid

About The Position

The IDEAS Security Data Science team focuses on building AI‑ and agent‑centric data science solutions that power Microsoft Security products and experiences. We partner closely with Security engineering and product teams to translate complex, high‑signal data into intelligent systems, automated decisioning, and measurable business and product impact. Our work spans AI agents, applied machine learning, security telemetry, experimentation, and executive‑ready insights that drive growth, protection, and operational excellence across the Security portfolio. As a Senior Data Scientist in Security Data Science, you will lead the development of AI‑first and agent‑driven analytical solutions by default. You will design and productionize models, agents, and data products that directly influence security product experiences, customer outcomes, and business decisions. This role requires strong applied ML skills, comfort working with LLM‑based systems, deep analytical rigor, and the ability to mentor others while operating in close partnership with engineering and product. This position is based at the Redmond campus with 3 days per week work in the office and 2 days per week work from home. Relocation assistance is available. 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.

Requirements

Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.
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.

Nice To Haves

Programming: Python, SQL (R optional)
Machine Learning & AI: supervised/unsupervised learning, model evaluation, applied ML systems
Statistics: experimentation, hypothesis testing, causal inference
Data Engineering: Spark, data pipelines, Azure Data Lake
Experience working with production data systems and distributed architectures.
Ability to explain complex technical topics to non‑technical audiences.
Experience building AI‑ or agent‑based systems, including LLM‑enabled workflows.
Familiarity with ML/AI deployment pipelines and observability.
Background working with security, trust, privacy, or compliance‑sensitive data.
Ability to influence decisions through clear, concise storytelling with data and metrics.

Responsibilities

Agent & AI‑Centric Solution Development (Primary Responsibility)
Design, build, and iterate on AI‑ and agent‑based solutions that operate by default in Security product workflows.
Develop intelligent systems using ML, LLMs, and retrieval‑augmented approaches to automate analysis, decisioning, and insight generation.
Partner with engineering and PM to productionize agents and AI features with real customer and business impact.
Define success metrics and telemetry for AI agents and continuously improve them using feedback loops.
Statistical Analysis & Experimentation
Design and execute controlled experiments to validate product and business hypotheses.
Apply advanced statistical techniques (e.g., regression, causal inference, Bayesian methods) to security and product data.
Clearly communicate uncertainty, limitations, and confidence to stakeholders.
Model Development & Deployment
Develop predictive and prescriptive models using machine learning and AI techniques.
Ensure models and agents are production‑ready, scalable, and aligned with privacy, security, and compliance requirements.
Monitor performance post‑deployment and iterate using telemetry and user feedback.
Business & Product Impact
Translate complex analytical and AI‑driven outputs into clear product and business recommendations.
Influence Security product strategy and prioritization through data and experimentation.
Collaborate cross‑functionally to align analytics, agents, and AI investments with organizational goals.
Data Engineering & Infrastructure
Build ad‑hoc and production‑grade data pipelines over large‑scale security and product telemetry.
Partner with Data Engineering teams to ensure secure, reliable, and scalable data infrastructure.
Implement best practices for data quality, governance, and observability.
Leadership & Mentorship
Coach and mentor junior data scientists on applied ML, experimentation, and AI‑first development.
Drive adoption of agent‑centric and AI‑native patterns across the team.
Contribute to standards for experimentation, metrics, and responsible AI usage.