Senior Data Scientist

Security represents the most critical priorities for our customers in a world awash in digital threats, regulatory scrutiny, and estate complexity. Microsoft Security aspires to make the world a safer place for all. We want to reshape security and empower every user, customer, and developer with a security cloud that protects them with end-to-end, simplified solutions. The Microsoft Security organization accelerates Microsoft's mission and bold ambitions to ensure that our company and industry are securing digital technology platforms, devices, and clouds in our customers' heterogeneous environments, as well as ensuring the security of our own internal estate. Our culture is centered on embracing a growth mindset, inspiring excellence, and encouraging teams and leaders to bring their best each day.

The Microsoft Identity Security data organization is responsible for building and operating large-scale data science, machine learning, and AI-driven systems that protect users and systems from compromise, fraud, and abuse across Microsoft platforms.

As a Senior Data Scientist, you will design, build, and ship production machine learning and applied AI systems, including classical ML detections and emerging LLM- and agent-based protections that stop malicious actors targeting billions of Microsoft customers. You will own problems end-to-end: framing the detection question, exploring telemetry at scale, building and evaluating models, driving them into production, and monitoring their real-world impact. You will partner closely with software engineering, product management and security researchers.

Do you love big data and the hard problems it creates? Do you want to apply your ML and applied AI skills to protect millions of customers from real adversaries? Then this role is for you.

• Design, develop, evaluate, and ship production ML models and applied AI systems (including LLM- and agent-based approaches) for compromise detection, fraud, and anti-abuse scenarios.
• Own detection problems end-to-end: problem framing, data exploration, feature engineering, modeling, offline/online evaluation, deployment, and post-launch monitoring.
• Analyze large-scale identity and security telemetry to identify attacker behavior, quantify risk, and uncover new detection opportunities.
• Define and run rigorous experiments (e.g., A/B tests, shadow evaluations) and establish quality, monitoring, and responsible-AI standards for the models you own.
• Partner with engineering to ensure models are scalable, reliable, observable, and maintainable in production.
• Collaborate with product management, security researchers, and partner teams to translate ambiguous security problems into clear metrics, roadmaps, and shipped solutions.
• Communicate insights, results, and tradeoffs clearly to technical and non-technical stakeholders, using strong data storytelling and visualization.
• Contribute to the technical growth of the team through design reviews, mentoring, and raising the engineering and science bar.

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.

Preferred Qualifications

• Demonstrated experience delivering ML models into production in partnership with software/data engineering teams.
• Strong programming skills in Python and experience with large-scale data processing (e.g., Apache Spark, SQL) on cloud-based platforms.
• Solid foundation in machine learning, statistical modeling, and experimentation methodology.
•  Proven ability to operate independently in ambiguous problem spaces and drive work to shipped outcomes.
• Experience applying data science / ML to security, fraud, abuse, or risk detection domains.
• Experience with AI agents, LLM-enabled systems, or advanced applied AI techniques (prompting, evaluation, retrieval, fine-tuning).
• Experience with cloud ML platforms, experimentation frameworks, and model lifecycle / monitoring tooling.
• Familiarity with responsible AI, privacy, and compliance considerations in production systems.
• Strong written and verbal communication; ability to influence cross-functional partners and senior stakeholders.

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 $158,400 - $258,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

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.