Principal Data & Applied Scientist

The Time + Places team is looking for a Principal Data & Applied Scientist. This role focuses on leveraging LLMs and developing advanced machine learning models and solutions to enhance time management, boost productivity, and improve workplace experiences for M365 customers in hybrid work environments.

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.

Responsibilities

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. Key responsibilities include: 

• 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 that optimize the 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 deliver optimal results.  
• 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.

Qualifications

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.
• 6+ years experience in Python, SQL, Databricks, Azure ML, Spark, and experience with large language models (LLMs), supervised/unsupervised learning, and natural language processing (NLP).
• 6+years experience in architecting and integrating machine learning models into customer-facing products, with a focus on optimizing relevance, personalization, and user engagement.

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: 

• Expereince to lead technical efforts and collaborate effectively with cross-functional teams (product, engineering).
• Expereince to present complex data science insights to both technical and non-technical stakeholders.
• Experience with workplace productivity tools, scheduling systems, or hybrid work solution