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Microsoft Senior Data Scientist India, Karnataka, Bengaluru 844194
Are you a data scientist, andin their disciplines and working together as one to bring the best practices of engineering and architecture to
(EAG) is a global consulting and engineering organization that supports ourmost complex and leading-edgeEAG enhances ISDs technical capabilities, and partners withothers to develop approaches, innovativesolutions, and engineeringexperience throughand trustworthy AI solutions.
Data Scientistexperience in
Required/Minimum Qualifications
- Minimum of8years(for those withBachelor’sDegree/Master’s Degree) or5years(for those with a Doctorate)of experience as a data scientist implementing data science solution, with experience in implementing projects in one or more areas amongst: Computer Vision, LLMs, Audio/Voice data processing, and Reinforcement Learning.
- Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, Engineering or related field AND data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
- Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science,Engineeringor related field data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience
- OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science,Engineeringor related field
- OR equivalent experience.
- Proficiencyin Englishis mandatory.Proficiencyin Mandarin or Cantonese is preferred.
the following key knowledge,and experience:
- Hands-on software engineering experience (e.g.Python, C++)
- Proven skills and experience in a data science role
- Familiarity with building and deploying largescale AI solutions into production within a cloud environment. Has experience in working withMLOps,LLMOpsand frameworks likeLangChain, Semantic Kernel and Prompt Flow.
- , directly and independently,inensuring that the proposed solution addresses the business needs – through all stagesfrom solutioning todeployment into production.
Understands problems facing projects andis able toleverage knowledge of data science to be able to uncover important factors that can influence outcomes on specific products.Describes the primaryfor completion. Assesses current situation for resources, risks,contingencies, requirements, assumptions, and constraints. Coaches less experienced engineers in standards and best practices.take actionon insights.
Data Preparation and Understanding
data necessary for successful completion of the project plan. Proactively detects changes and communicatessenior leads. Develops
and Statistical Analysis
Leverages knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, NLP, image recognition, etc.) and individual algorithms (e.g., linear and logistic regression, k-means, gradient boosting, autoregressive integrated moving average [ARIMA], recurrent neutral networks [RNN], long short-term memory [LSTM] networks) to identify the best approach to complete objectives. Understands modeling techniques (e.g., dimensionality reduction, cross validation, regularization, encoding, assembling, activation functions) and selects the correct approach to prepare data,appropriate languageand solutions, as well as established patterns and
. Defines and designs feedback and evaluation methods. Coaches and
,capabilities within existing systems. Shares knowledge of the industry through conferences, white papers, blog posts, etc.deep knowledge of industry trends, technologies, and advances. Actively contributes to the body of thought leadership and intellectual property (IP) best practices.
Coding and Debugging
Writes efficient, readable, extensible code from scratch that spans multiple features/solutions. Develops technicalin proper modeling, coding, and/or debugging techniques such asexperienced engineers in better understanding coding and debugging best practices. Builds professional-grade documents for knowledge transfer and deployment of predictive analytic models. Leverages technicalof big-data software engineering concepts, such as Hadoop Ecosystem, Apache Spark, continuousand continuous delivery (CI/CD), Docker, Delta Lake,, AML, and representational state transfer (REST) application programming interface (API) consumption/
Embody our
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