Senior Applied Scientist at Microsoft

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

03 Mar, 26

Salary

0.0

Posted On

03 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Machine Learning, Deep Learning, LLMs, Pytorch, Tensorflow, Statistical Analysis, Predictive Analytics, Research, Model Evaluation, Dataset Creation, Collaboration, Problem Solving, Cloud Pipelines, AI Product Development, Publications

Industry

Software Development

Description

Driving projects from design through implementation, experimentation and finally shipping to our users. This requires deep dive into data to identify gaps, come up with heuristics and possible solutions, using LLMs to create the right model or evaluation prompts, and setup the engineering pipeline or infrastructure to run them. Come up with evaluation techniques, datasets, criteria and metrics for model evaluations. These are often SOTA models or metrics / datasets. Hands on own the fine-tuning, use of language models, including dataset creation, filtering, review, and continuous iteration. This requires working in a diverse geographically distributed team environment where collaboration and innovation are valued. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 7+ 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 5+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ year(s) related experience (e.g., statistics, predictive analytics, research) Prior experience with data analysis or understanding, looking at data from a large scale systems to identify patterns or create evaluation datasets. Experience with common machine learning, deep learning frameworks and concepts, using use of LLMs, prompting. Experience in pytorch or tensorflow is a bonus.Ability to communicate technical details clearly across organizational boundaries. These requirements include but are not limited to the following specialized security screenings: 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 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers). Experience presenting at conferences or other events in the outside research/industry community as an invited speaker. 3+ years experience conducting research as part of a research program (in academic or industry settings). 1+ year(s) experience developing and deploying live production systems, as part of a product team. 1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping. Solid ability and effectiveness working end-to-end in a challenging technical problem domain (plan, design, execution, continuous release, and service operation). Some prior experience in applying deep learning techniques and drive end-to-end AI product development (Search, Recommendation, NLP, Document Understanding, etc). Prior experience with Azure or any other cloud pipelines or execution graphs. Self-driven, results oriented, high integrity, ability to work collaboratively, solve problems with groups, find win/win solutions and celebrate successes. Customer/End-result/Metrics driven in design and development.Keen ability and motivation to learn, enter new domains, and manage through ambiguity. Solid publication track records at top conferences like ACL, EMNLP, SIGKDD, AAAI, WSDM, COLING, WWW, NIPS, ICASSP, etc.

Responsibilities

Drive projects from design through implementation and experimentation to shipping. Identify data gaps and create models or evaluation prompts using LLMs.