Principal Applied Scientist

The Signals Team at Microsoft Ads develops advanced machine learning models to predict user responses to advertisements. These predictive signals play a crucial role in enhancing user engagement and maximizing advertiser returns.

As a Principal Applied Scientist on our team, you will design and implement large-scale machine learning models, driving their deployment into production and translating your expertise into tangible revenue impact. This role offers a unique opportunity to enhance your skills in building and deploying machine learning models at scale. You will gain invaluable experience analyzing model behavior within a complex, multi-layered model stack, and understanding the intricate interactions between various algorithms. Additionally, you will stay at the forefront of state-of-the-art approaches in this domain.

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.

• Build large scale machine learning models for text and numerical data, deploy models in production.
• Run A/B testing, analyze impact of model metrics and possible model failure modes on business KPIs.
• Conduct data exploration and analysis of new features and model improvements.
• Establish scalable pipelines for data processing and analytics, model training and validation.
• Establish automated processes to monitor system health using agentic workflows.
• Track relevant state-of-the-art approaches in the field, identify and implement applications to improve the modeling stack.
• Mentor less experienced scientists on the team.

Required Qualifications:

• Bachelor'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 Master'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 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.

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 9+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 5+ years customer-facing, project-delivery experience, professional services, and/or consulting experience.
• Machine Learning Model Expertise: Deep experience designing, training, and optimizing large-scale machine learning models, particularly transformer-based models and large language models, with a strong focus on production deployment and performance.
• LLM Inference & Fine-Tuning: Proven ability to build and optimize high-performance LLM inference pipelines and apply advanced fine-tuning techniques (e.g., SFT, LoRA, RLHF) to adapt foundation models for domain-specific use cases.
• Model Quality & Evaluation: Strong expertise in developing robust evaluation frameworks for ML and LLM systems, including automated metrics, human evaluation, benchmarking, and monitoring for issues such as hallucination, bias, safety, and model regression.

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