Applied Scientist

Microsoft 365 offers a powerful suite of tools designed to empower users to achieve more. Our services include Word and Excel for productivity, Teams for seamless communication and collaboration, and Microsoft Copilot for AI-driven assistance. Together, these solutions streamline workflows and enhance efficiency for millions of users and organizations worldwide. 

At Microsoft 365 Customer Success Engineering, we are dedicated to creating experiences that help our customers maximize the value of our products. We are seeking motivated and enthusiastic AI practitioners eager to develop innovative solutions that tackle real-world challenges and delight our customers, ultimately driving their success. 

Successful candidates will join a newly formed team of Applied Scientists on an exciting mission to deliver content excellence by leveraging advanced AI technologies to transform the way content is created, reviewed, and maintained. We value intellectual curiosity, critical thinking, and expertise in artificial intelligence. A strong aptitude for learning and applying AI solutions is essential. Team members will implement these solutions in a commercial environment, leveraging the Azure Cloud technology stack. 

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.

Applied Scientists on the team are responsible for contributing to all stages of our project lifecycle including: 

• Problem Definition: Clearly defining the problem, aligning business goals and desired AI outcomes.
• Data Processing: Collect and clean datasets ensuring quality and readiness for modeling.
• Exploratory Data Analysis: Analyze data to identify patterns guiding feature selection.
• Feature Engineering: Leverage domain knowledge to develop features to boost model performance.
• Model Development: Implement machine learning algorithms to optimize model performance.
• Model Validation: Use appropriate metrics to evaluate model performance on unseen data.
• Model Deployment: Integrate the model into production systems for efficient operation.
• Monitoring and Iteration: Continuously track model performance and iterate based on feedback.
• Results Communication: Report insights and successes through real-time dashboards.
• Maintenance: Establish and manage the cloud environment using DevOps practices to ensure stability and scalability.

Required Qualifications: 

• Possess a foundation in machine learning, supported by demonstrable experience in either industry or academia.

Preferred Qualifications: 

• Strong grasp of ML concepts, able to explain both theory and practical applications.
• Proficient in NLP techniques, with demonstrated success in project implementations.
• Effectively translates academic knowledge into solutions for real-world problems.
• Skilled in data analysis using Python, Pandas, and Jupyter Notebooks or similar.
• Proficient in .NET or similar, with additional expertise in Python.
• Preferred experience in team-based development, utilizing Git for version control and managing release cycles.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

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