Microsoft

Builds workplace software, PCs and cloud services

Senior Data Science

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeNo salary listedIn-person

About the Job

Requirements

A Master's degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field is required.
At least 3 years of data-science experience, including managing structured and unstructured data, applying statistical techniques, and reporting results.

Responsibilities

Identify project problems and use data science to uncover factors that can influence product outcomes.
Acquire data needed to complete project plans.
Select modeling and machine-learning approaches appropriate to project objectives.
Relate technical metrics to business objectives and provide technical feedback that helps identify potential business opportunities.
Write efficient, readable, extensible code from scratch across multiple features and solutions.
Collaborate with customers and internal cross-functional stakeholders to understand business needs.
Define metrics and evaluation frameworks with partners for complex AI/large language model systems and agentic product performance.
Diagnose complex AI/large language model system issues and identify opportunities for improvement; serve as a resource for junior team members.
Select appropriate models and prompting approaches for tasks.
Prototype and deploy AI model applications in containerized environments, including Azure AI Foundry and LangChain, to operationalize, build, and validate scalable tools and solutions for internal customers.
Optimize scalable systems using context-engineering workflows such as retrieval-augmented generation, selecting data sources, retrieval systems, and generative models to ensure quality output.
Drive AI system roadmaps through ongoing model-performance assessment and refinement of large language model systems and models.
Formulate project roadmaps that lead to measurable improvement in business performance metrics.
Influence stakeholders to implement solution improvements that deliver business value, using storytelling, visualizations, and other influencing tools.
Apply and enforce team standards related to bias, privacy, and ethics.
Create project plans specifying required completion steps and assess resources, risks, contingencies, requirements, assumptions, and constraints.
Interpret business strategy briefings and articulate data-driver strategies for industries or cross-industry functions, including sales and marketing, operations, and data monetization.
Engage business stakeholders to capture and shape data-driven methods applicable to their value chains.
Lead customer discussions to understand and solve business problems and workflows in products or solutions, and identify gaps.
Develop expertise in modeling, coding, and debugging techniques, including locating, isolating, and resolving errors and defects; prevent common defects using best practices.
Collaborate with other teams and apply their best practices to the team's work.
Mentor and guide less experienced engineers in coding and debugging best practices.
Create professional-grade documents for knowledge transfer and predictive-analytic-model deployment.
Apply big-data software engineering concepts and tools, including the Hadoop ecosystem, Apache Spark, continuous integration and continuous delivery, Docker, Delta Lake, MLflow, Azure Machine Learning, and REST API consumption and development.
Understand customer needs and perspectives, validate them, and consider the broader customer organization and context.
Promote customer adoption by delivering model solutions and supporting customer relationships.
Help customers overcome obstacles, develop practical tailored solutions, and ensure proper execution.
Build customer trust by applying interpretability and knowledge of Microsoft products and solutions.
Set realistic customer expectations, including explaining limitations of their data.
Detect changes in datasets or anticipated outcomes, such as product or consumer behavior, and communicate them to senior leads or leaders.
Develop usable modeling datasets and use AI tools such as Codex and GitHub Copilot to identify process gaps and improve data quality.
Contribute to ethics and privacy policies for data collection, data preparation, and AI-tool use by suggesting updates to internal best practices.
Contribute to customer discussions about data integrity and cleanliness.
Link selected models to business objectives and evaluate how well models and measures meet those objectives and desired solutions.
Define and design feedback and evaluation methods.
Coach and mentor internal stakeholders, including software engineering managers and technical program managers, as needed.
Present results and findings to senior internal and external stakeholders.
Develop understanding of the team's and other teams' work and propose collaboration opportunities.
Coach and support teams in executing strategy, and leverage capabilities in existing systems.
Share industry knowledge through conferences, white papers, and blog posts; research and maintain knowledge of industry trends, technologies, and advances; contribute to thought leadership and intellectual-property best practices.
Apply statistical analysis, deep learning, causal modeling, and reinforcement learning to identify suitable approaches for objectives.
Use modeling techniques such as validation, hypothesis testing, and optimal data use, and machine-learning approaches such as supervised and unsupervised learning, estimation, and forecasting, to prepare data, train and optimize models, and evaluate statistical and business significance.
Assess risks including data leakage, bias-variance tradeoffs, and methodological limitations.
Write scripts in appropriate languages, including T-SQL, U-SQL, KQL, Python, and R.
Construct hypotheses, design controlled experiments, analyze results using statistical tests, and communicate findings to business stakeholders.
Communicate with diverse audiences about data-quality issues and initiatives.
Account for model-deployment considerations including performance, scalability, monitoring, maintenance, integration into production engineering systems, and stability.
Develop operational models that run at scale in partnership with engineering teams.
Coach less experienced engineers on data-analysis and modeling best practices.
Develop knowledge of Microsoft's AI and machine-learning tools, including Azure Machine Learning, Azure Cognitive Services, and Azure Databricks.
Explain complex statistics and machine-learning topics clearly to customers and stakeholders.
Guide model operationalization within project approaches using existing technologies, products, solutions, and established patterns and practices.
Stay current on embedding models and vector databases.

Desired Qualifications

A Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field.

Overview

Identifies and understands problems facing projects and is able to leverage knowledge of data science to uncover important factors that can influence outcomes on specific products. Acquires data necessary for successful completion of the project plan. Leverages knowledge of modeling and machine learning solutions to identify the best approach to complete objectives. Understands relationship between technical metrics and business objectives. Uses business knowledge and technical expertise to provide feedback to the engineering team to identify potential future business opportunities. Writes efficient, readable, extensible code from scratch that spans multiple features/solutions. Collaborates with end customer and Microsoft internal cross-functional stakeholders to understand business needs. Applies a customer-oriented focus by understanding customer needs and perspectives, validating customer perspectives, and focusing on broader customer organization/context. Collaborates with relevant partners on defining metrics and frameworks for evaluating complex AI/LLM systems and agentic product performance.

Responsibilities

AI Operations

Collaborates with relevant partners on defining metrics and frameworks for evaluating complex AI/large language model (LLM) systems and agentic product performance. Diagnoses complex issues and identifies opportunities to drive improvements within AI/LLM systems within domain areas, acting as a resource for junior team members. Selects the appropriate model (e.g., LLM, prompting approach) for tasks. Prototypes and deploys applications of AI models using containerized environments (e.g., AI Azure Foundry, LangChain) to operationalize, build, and validate scalable tools/application/solutions for use by internal customers. Optimizes scalable systems that use optimal context engineering (e.g., retrieval-augmented generation) workflows, including the selection of data sources, retrieval systems, and generative models to ensure quality output. Drives AI system roadmaps through continual assessment of model performance and refinement of LLM systems and models.

Business Management

Collaborates with end customer and Microsoft internal cross-functional stakeholders to understand business needs. Formulates a roadmap of project activity that leads to measurable improvement in business performance metrics over time. Influences stakeholders to make solution improvements that yield business value by effectively making compelling cases through storytelling, visualizations, and other influencing tools. Exemplifies and enforces team standards related to bias, privacy, and ethics.

Business Understanding and Impact

Identifies and understands problems facing projects and is able to leverage knowledge of data science to uncover important factors that can influence outcomes on specific products. Produces a project plan to specify necessary steps required for completion. Assesses current situation for resources, risks, contingencies, requirements, assumptions, and constraints. Coaches less experienced engineers in data standards and best practices. Understands business strategy briefings and articulates data driver strategies for specific industries or cross-industry functions, such as: Sales/Marketing, Operations, and new Data Monetization Schemes. Engages business stakeholders to capture and shape their thinking on data-driven methods applicable to their value chain. Leads customer conversations to understand, define, and solve business problems and business workflows in products or solutions to identify gaps.

Coding and Debugging

Writes efficient, readable, extensible code from scratch that spans multiple features/solutions. Develops technical expertise in proper modeling, coding, and/or debugging techniques such as locating, isolating, and resolving errors and/or defects. Understands the causes of common defects and uses best practices in preventing them from occurring. Collaborates with other teams and leverages best practices from those teams into work of their own team. Mentors and guides less experienced engineers in better understanding coding and debugging best practices. Builds professional-grade documents for knowledge transfer and deployment of predictive analytic models. Leverages technical proficiency of big-data software engineering concepts, such as Hadoop Ecosystem, Apache Spark, continuous integration and continuous delivery (CI/CD), Docker, Delta Lake, MLflow, AML, and representational state transfer (REST) application programming interface (API) consumption/development.

Customer/Partner Orientation

Applies a customer-oriented focus by understanding customer needs and perspectives, validating customer perspectives, and focusing on broader customer organization/context. Promotes and ensures customer adoption by delivering model solutions and supporting relationships. Works with customers to overcome obstacles, develops tailored and practical solutions, and ensures proper execution. Builds trust with customers by leveraging interpretability and knowledge of Microsoft products and solutions. Helps drive realistic customer expectations, including information about the limitations of their data.

Data Preparation and Understanding

Acquires data necessary for successful completion of the project plan. Proactively detects changes in datasets or anticipated outcomes (e.g., product/consumer behavior) and communicates to senior leads or leaders. Develops usable data sets for modeling purposes by leveraging AI tools (e.g., Codex, GitHub Copilot) to identify gaps in current processes and improve data quality. Contributes to ethics and privacy policies related to collecting data, preparing data, and using AI tools (e.g., Codex, GitHub Copilot) by providing updates and suggestions around internal best practices. Contributes to data integrity/cleanliness conversations with customers.

Evaluating for Insight and Impact

Understands relationship between technical metrics and business objectives. Ensures clear linkage between selected models and desired business objectives. Evaluates and identifies the degree to which models and measures meet business objectives and desired solutions. Defines and designs feedback and evaluation methods. Coaches and mentors internal stakeholders (e.g., software engineering managers or technical program managers) as needed. Presents results and findings to senior internal and external stakeholders.

Industry and Research Knowledge / Opportunity Identification

Uses business knowledge and technical expertise to provide feedback to the engineering team to identify potential future business opportunities. Develops a better understanding of work being done on team, and the work of other teams to propose potential collaboration efforts. Coaches and provides support to teams to execute strategy. Leverages capabilities within existing systems. Shares knowledge of the industry through conferences, white papers, blog posts, etc. Researches and maintains deep knowledge of industry trends, technologies, and advances. Actively contributes to the body of thought leadership and intellectual property (IP) best practices.

Modeling and Statistical Analysis

Leverages knowledge of modeling and machine learning solutions (e.g., state-of-the-art statistical analysis, deep learning, causal modeling, reinforcement learning) to identify the best approach to complete objectives. Understands modeling techniques (e.g., validation, hypothesis testing, optimal use of data) and machine learning (e.g., supervised and unsupervised learning, estimation and forecasting) in order to select the correct approach to prepare data, train and optimize the model, and evaluate the output for statistical and business significance. Understands the risks of data leakage, the bias/variance tradeoff, methodological limitations, etc. Writes all necessary scripts in the appropriate language: T-SQL, U-SQL, KQL, Python, R, etc. Constructs hypotheses, designs controlled experiments, analyzes results using statistical tests, and communicates findings to business stakeholders. Effectively communicates with diverse audiences on data quality issues and initiatives. Understands operational considerations of model deployment, such as performance, scalability, monitoring, maintenance, integration into engineering production system, stability. Develops operational models that run at scale through partnership with engineering teams. Coaches less experienced engineers on data analysis and modeling best practices. Develops a strong understanding of the Microsoft toolset in artificial intelligence (AI) and machine learning (ML) (e.g., Azure Machine Learning, Azure Cognitive Services, Azure Databricks). Breaks down complex statistics and machine learning topics into manageable topics to explain to customers and other stakeholders. Provides guidance on model operationalization that is built into the project approach using existing technologies, products and solutions, as well as established patterns and practices. Understands and keeps up to date on embedding models and vector databases.

Qualifications

Required/minimum qualifications

Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)

Additional or preferred qualifications

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field

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.

About the Company

Microsoft

Builds workplace software, PCs and cloud services

Microsoft builds workplace software, operating systems and cloud infrastructure for consumers, businesses and government agencies, from small organizations to global enterprises. Microsoft 365 combines Word and Excel with Teams communication and Copilot AI features, while Windows runs PCs and Surface supplies the company's own computers. Azure rents computing capacity, storage and databases to build and run web applications. Microsoft also sells Xbox consoles and games, collecting revenue through software licenses, subscriptions, usage charges and product sales. Founded in 1975 by Bill Gates and Paul Allen, Microsoft is headquartered in Redmond, Washington.

Company size: 10,001+
Company stage: IPO
Headquarters: Redmond, WA
Founded: 1975

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