Microsoft Principal Data & Applied Scientist 

United States, Washington 

827761773

The Time + Places team is looking for aPrincipal Data & Applied Scientist

The position involves developing and integrating machine learning models, creating self-service reporting platforms for stakeholders, and delivering data-driven insights to solve complex business problems. It also includes defining metrics to evaluate model performance and ensuring that solutions align with business goals, scale effectively, and meet quality standards.

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.

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ 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 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 5+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
    
• Deepexpertisein Python, SQL, Databricks, Azure ML, Spark, and experience with large language models (LLMs), supervised/unsupervised learning, and natural language processing (NLP).
• Experience in architecting and integrating machine learning models into customer-facing products, with a focus onoptimizingrelevance, personalization, and user engagement.

Preferred Qualifications:

• Experience with workplace productivity tools, scheduling systems, or hybrid work solutions.
• Familiarity with real-time data feedback loops and experienceoptimizingmodels based on user feedback.
• Ability to lead technical efforts and collaborate effectively with cross-functional teams (product, engineering).
• Good Communicationskills to present complex data science insights to both technical and non-technical stakeholders.
• Expertisein fine-tuning LLMs and implementing RAG techniques to improve model performance.
• Prior working experience with Enterprise products is a plus.

Applied Sciences IC6 - The typical base pay range for this role across the U.S. is USD $161,600 - $286,200 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 $209,600 - $314,400 per year.Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:Microsoft will accept applications for the role until January 20, 2025.

• Machine Learning Innovation: Lead the development of advanced machine learning models that address user needs in time management and hybrid work settings. Use LLMs and other data sources (meeting data, documents, and emails) to create solutions for meeting prioritization, scheduling, and feature quality evaluations.
• Relevance & Personalization Models: Architect and refine both supervised and unsupervised models thatoptimizethe relevance of key features within Microsoft Calendar and Microsoft Places. Improve meeting scheduling and hybrid work experiences by extracting meaningful signals from meeting titles, agendas, documents, and participants.
• Collaborate on Product Development: Partner closely with product and engineering teams to translate user needs into actionable machine learning solutions. Ensure models are effectively integrated into products, meeting scalability, quality, and real-time performance requirements.
• Utilize Industry-Leading Tools: Access Microsoft’s vast data scale, computing resources, and advanced machine learning frameworks to deliver high-impact solutions. Apply prompt optimization, fine-tuning, and retrieval-augmented generation (RAG) techniques to ensure models deliveroptimalresults.
• Performance Metrics: Define, track, and refine key performance metrics for machine learning models. Continuously iterate on models based on user feedback and real-time data to improve accuracy, precision, and recall.