Senior Applied Scientist - Ads Ranking & Retrieval
Microsoft · Bangalore, India · 1d ago
Role: Senior Applied Scientist
The AI Economy Team at Microsoft is building the infrastructure that powers how organizations ground, deploy, and scale AI applications. Developers can access it through self-serve, programmatic access, and access at that scale attracts abuse. The AI Economy Trust & Safety team builds the detection behind those access decisions: verifying that customers are who they claim to be, recognizing when many accounts are one actor, and separating ordinary heavy usage from extraction, resale and automated exploitation.
You will build detection models that protect a large-scale, developer-facing Microsoft platform from fraud and abuse by resolving many accounts to a single actor, separating legitimate customers from fabricated ones at signup, and monitoring account behavior after activation. This work involves sparse and delayed labels, evolving abuse patterns, and rigorous evaluation before deployment.
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.
Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
Responsibilities
- Develop and productionize identity-clustering models using similarity, graph, linkage and embedding methods.
- Develop applicant-risk models using structured and unstructured identity, business and behavioral signals.
- Build behavioral detection for active accounts using sequence modeling, anomaly detection, query-distribution analysis and cross-account signals.
- Train, tune and validate detection models, including feature engineering and adversarial testing of attacker-controlled inputs.
- Address weak supervision, delayed and noisy labels, class imbalance, model calibration, operating-point selection and drift.
- Investigate live abuse patterns, conduct offline and online experiments, and turn findings into reusable detections and measurable coverage improvements.
- Work with product and engineering counterparts on the WebIQ team to productionize model decisions, and provide technical analysis for privacy and legal reviews.
Qualifications
Required/Minimum 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.
Additional or preferred qualifications
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:
- 5+ years experience developing machine learning models in a production environment, including coding in Python, R, Scala, or a comparable language.
- Published or applied work in adversarial machine learning, anomaly detection, or bot and automation detection.
- Experience with API abuse, scraping, or model extraction and distillation as a defender.
- Familiarity with privacy and compliance constraints on identity signals used for risk decisions.
- Depth in one or more of: graph neural networks or embedding-based entity linkage, sequence and time-series models of account behavior, or LLM-assisted enrichment and triage of sparse evidence.
- Solid grounding in experimental design, causal inference and model calibration, including selecting and explaining production operating points.
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.