Senior Data Scientist at Microsoft

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

10 Apr, 26

Salary

0.0

Posted On

10 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Python, R, Scala, SQL, Generative AI, Statistical Techniques, Data Wrangling, Feature Engineering, A/B Testing, Cloud Technologies, Big Data Solutions, Cross-Functional Collaboration, Communication Skills, Problem-Solving

Industry

Software Development

Description

Overview Viva Learning is on a mission to empower individuals, teams, and organizations with the skills they need to thrive in the age of AI and Copilot. We are building intelligent learning agents that deliver personalized, task-based microlearning experiences embedded in the flow of work. As a Senior Data Scientist, you will play a pivotal role in shaping the intelligence layer of Viva Learning - leveraging data from Microsoft Graph, M365, and external learning platforms to drive hyper-personalized learning journeys. 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 Design and implement predictive and prescriptive models to personalize learning content based on user roles, skills, and context. Collaborate with product managers, engineers, and UX researchers to translate business needs into analytical solutions. Develop scalable ML pipelines and integrate them into Viva Learning’s backend systems. Conduct experiments to evaluate model performance and iterate based on feedback and telemetry. Evangelize data science best practices across the team and mentor junior data scientists. Partner with internal teams (e.g., Azure, Graph, M365) to align on data strategy and infrastructure. Contribute to the development of learnability scores and semantic search enhancements for course recommendations. Explore and apply Generative AI techniques (e.g., LLMs, prompt engineering, fine-tuning) to enhance learning experiences, automate content generation, and improve semantic understanding. Qualifications Required Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. 5+ years of experience in applied data science, including building and deploying ML models at scale. Proficiency in Python, R, or Scala, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow). Strong SQL skills and experience with data wrangling and feature engineering. Hands-on experience with Generative AI models (e.g., GPT, T5, BERT) and frameworks like Hugging Face or OpenAI. Track record leading experimentation (A/B testing/causal inference) and building productionready models, including operationalization/monitoring. Experience in applying analytical and problem-solving skills. Solid background working in cross-functional teams. Ability to communicate technical concepts and quantitative analysis in a clear, precise, and actionable manner to non-technical audiences. 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: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. 5+ years of experience in applied data science, including building and deploying ML models at scale. Experience in learning platforms, HR tech, or employee experience domains. Experience working with cloud-based technologies and big data solutions (e.g., Hadoop, Spark). Familiarity with Microsoft Graph, Viva Learning, or M365 ecosystem. Exposure to semantic search, knowledge graphs, or Copilot integration. Publications or patents in applied machine learning or AI. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

Design and implement predictive models to personalize learning content and collaborate with cross-functional teams to translate business needs into analytical solutions. Develop scalable ML pipelines and conduct experiments to evaluate model performance.