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Microsoft

Data & Applied Scientist

Microsoft, Little Ferry, New Jersey, us, 07643


Do you want to be part of a growing global team creating solutions to critical business problems across multiple applications and service platforms? We are looking for Data & Applied Scientists I and Data & Applied Scientist II with a range of talents who will help create innovative, scalable & automated solutions as well as delightful end user experiences using generative AI and Copilot functionality.

Copilots have emerged as transformative technologies that boost productivity and thus empower every person and every organization on the planet to achieve more. To drive these generative Artificial Intelligence experiences, Microsoft relies on ambitious advances in Artificial Intelligence research and applying them to our products.

Dynamics 365 is one of the fastest growing SaaS Enterprise Resource Planning (ERP) portfolios in the world with capabilities across Finance, Supply Chain Management, Project Operations, Commerce, and Human Resources. We specialize in best-in-class solutions enabling businesses to run their end-to-end operations for the Dynamics 365 line of ERP software supporting a wide variety of industries.

ResponsibilitiesContribute to Microsoft Copilot development by building multi-model solutions using OpenAI as well Open Source Large Language Models (LLMs) to real-world business problems that delight our customers.Participate in end-to-end machine learning model lifecycle, from prototyping, implementing & evaluating various Machine Learning models (traditional, Deep Learning or Large Language Models), followed by deployment and monitoring using Azure tools.Execute Machine learning Operations (MLOps) with emphasis on timely collaboration, communication, response, delivery & satisfaction for all internal & external stakeholders.Designing and analyzing controlled experiments and observational analyses using causal inference techniques.Be accountable for the highest standards of AI ethics, including following all Microsoft policies around handling data and AI model building.QualificationsRequired/Minimum QualificationsBachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND relevant internship experience (e.g., statistics, predictive analytics, research).OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field.OR equivalent experience.Experience in core Machine Learning, Data Science concepts with hands-on experience with designing of experiments, quick prototyping, and evaluation.Experience with Machine Learning tool(s) to build models and analyze data (e.g. PyTorch, TensorFlow, scikit-learn).Additional Or Preferred QualificationsBachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research).Demonstrated coding skills and hands-on experience working with Large Language Models (LLMs) and familiarity with popular prompt-based methods.Experience using SQL or other query languages and doing data analysis, statistics, and modeling in Python or R.Experience working with large data sets (e.g. using big-data tools like Spark, Horovod, Petastorm) or doing large scale quantitative analysis and predictive modeling work.Experience using Visual Studio, VS Code, GitHub, Azure DevOps, and Azure cloud services in development and production scenarios.Microsoft is an equal opportunity employer. Consistent with applicable law, 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.

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