Microsoft Sr Data Applied Scientist 

India, Karnataka, Bengaluru 

816874919

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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.
• 3+ years of Experience with design and implementation of enterprise-scale AI products.

Other Requirements:

• Ability to meet Microsoft, customer and/or government security screening requirements is 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:

• Experience delivering Dynamics 365 and/or Power Platform solutions.

• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch) to develop production-grade quality products.
• Publication track records at top conferences like ACL, EMNLP, SIGKDD, AAAI, WSDM, COLING, WWW, NIPS, ICASSP, etc.

• Excellent problem-solving skills and the ability to work independently and collaboratively.

• Drive thought leadership, architecture for high scale, high throughput, low latency inferencing.
• Drive AI projects through their entire life cycle from idea creation through applied research, implementation, experimentation and finally to worldwide availability.
• You will be expected to meet with stakeholders/PM to gather the requirements and collaborate with cross-functional teams, including software engineers, to implement E2E solutions.
• Conduct experiments to evaluate model performance (including Large Language Models).
• Explore novel techniques and approaches to enhance model capabilities. Record ongoing work and experimental findings, sharing them to encourage innovation.
• Optimize model performance, scalability, and efficiency and deploy to production.
• Monitor model performance, troubleshoot issues, and iterate improvements.
• Stay up to date with the latest advancements in LLM, NLP, deep learning, and AI research.
• Be involved in the onboarding process for new team members, providing guidance and support as they join the team.
• Embody our,