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Senior Data Scientist at Microsoft
Bengaluru, karnataka, India -
Full Time

Start Date

Immediate

Expiry Date

23 Feb, 26

Salary

0.0

Posted On

25 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Python, Machine Learning, Deep Learning, NLP, Statistics, Generative AI, Cloud-Scale Systems, Data Processing, Data Science, A/B Testing, Model Performance Evaluation, Collaboration, Communication, Productization of AI, Teamwork

Industry

Software Development

Description
Research and productization of state-of-the-art in AI for Software Engineering. Build and manage large-scale AI experiments and models. Drive experimentation through A/B testing and offline validation to evaluate model performance. Stay up to date with the research literature and product advances in AI for software engineering Undergraduate degree in Computer Science, Engineering, Mathematics, Statistics. 6+ years experience with strong proficiency in Python, machine learning frameworks, and experience developing and deploying generative AI or machine learning models into production. (Experience with additional programming languages is a plus but not required.) Masters or PhD degree in Computer Science, Statistics, or related fields (undergraduates with significant appropriate experience will be considered). Strong professional experience in statistics, machine learning, including deep learning, NLP, econometrics. Experience in building cloud-scale systems and experience working with open-source stacks for data processing and data science is desirable. Experience with LLMs in natural language, AI for code or related fields Excellent communication skills, ability to present and write reports, strong teamwork and collaboration skills. Experience in productizing AI and collaborating with multidisciplinary teams.
Responsibilities
The role involves researching and productizing state-of-the-art AI technologies for software engineering. Additionally, the candidate will build and manage large-scale AI experiments and models, driving experimentation through A/B testing and offline validation.