Principal Data Scientist

Microsoft•Redmond, WA

About The Position

Do you enjoy shaping business value at scale with advanced analytics, influencing strategy for Microsoft’s most strategic customers, and setting technical direction that others adopt? Do you thrive as a hands-on technical leader, trusted advisor to senior executives, and mentor for the next generation of Data Scientists? You’ll turn ambiguous business problems into durable, repeatable data science approaches that improve delivery quality across teams and industries. The Industry Solutions Delivery (ISD) Engineering & Architecture Group (EAG) is a global consulting and engineering organization that supports Microsoft’s most complex and leading-edge customer engagements. As a Principal Data Scientist you will combine technical knowledge with broad strategic influence across multiple customer engagements, solution areas, and cross-functional teams. You will shape data science strategy across high-impact engagements, define reusable patterns and standards, and partner across engineering, architecture, and business teams to accelerate delivery quality, customer outcomes, and intellectual property (IP) creation grounded in real customer delivery experience. At Microsoft, our mission to empower every person and every organization on the planet to achieve more guides how we partner with customers to deliver trusted, impactful solutions. With a growth mindset culture, we innovate responsibly and measure success by shared progress across people, teams, and customers. Join us to help shape what great AI and data science delivery looks like across customers, industries, and Microsoft teams.

Requirements

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.

Nice To Haves

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.

Responsibilities

Drives alignment between customer business priorities and data science strategy across complex engagements, solution areas, or industry scenarios. Frames ambiguous business problems into scalable data science opportunities and defines approaches that balance time to value, technical feasibility, risk, and long-term maintainability. Makes high-judgment recommendations on solution direction, methodological tradeoffs, and delivery priorities where decisions affect multiple stakeholders, workstreams, or long-term platform choices. Assesses resources, dependencies, risks, assumptions, and constraints across multiple workstreams and uses that judgment to influence direction and prioritization. Uses deep understanding of organizational dynamics, cross-team interdependencies, schedule constraints, and resource tradeoffs to drive action from partners and senior stakeholders. Translates business strategy into data and AI strategies for specific industries and cross-industry functions such as Sales, Marketing, Operations, and data monetization. Leads senior customer conversations to define problems, shape solution direction, and identify reusable patterns that can improve outcomes beyond a single engagement. Raises the bar for others through guidance on standards, decision frameworks, and best practices.
Defines the data readiness strategy for complex engagements by establishing expectations for data quality, fitness for purpose, lineage, governance, and ongoing maintainability. Guides teams and customers in identifying the data required to achieve business outcomes and highlights material gaps, risks, and tradeoffs early. Establishes repeatable approaches for assessing and improving data usability for modeling, experimentation, and operationalization. Drives conversations with customers and internal stakeholders on data integrity, instrumentation, privacy, compliance, and responsible data use. Proactively identifies changes in data availability, quality, or business context and adjusts technical direction accordingly. Shapes internal best practices for collecting, preparing, and governing data so they can be adopted consistently across engagements.
Defines modeling strategies for ambiguous, high-impact business problems and selects approaches that appropriately balance performance, interpretability, scalability, operational complexity, and risk. Applies deep knowledge across machine learning and statistical methods such as classification, regression, clustering, forecasting, natural language processing, and computer vision, and guides teams on when to use bespoke approaches versus repeatable platform-based solutions. Establishes methodological standards for feature engineering, validation design, regularization, experimentation, optimization, and evaluation, including practices around leakage prevention, bias/variance tradeoffs, robustness, and model limitations. Uses code and experimentation fluently in languages and tools such as Python, R, T-SQL, KQL, and related platforms when depth is needed to resolve high-risk technical questions or unblock delivery. Designs hypotheses and experiments, interprets results with statistical and business rigor, and communicates implications clearly to technical and non-technical stakeholders. Defines patterns for productionization, including monitoring, stability, scalability, integration, lifecycle management, and partnership with engineering teams. Builds and promotes reusable reference approaches for model operationalization using Microsoft technologies and established engineering practices. Provides technical leadership to data scientists, engineers, and architects by setting the standard for sound modeling decisions and explaining complex concepts in practical, customer-relevant terms.
Defines evaluation frameworks that connect model performance, business impact, operational health, and responsible AI requirements. Ensures that success criteria are explicit, measurable, and aligned to customer objectives before and throughout delivery. Establishes launch-readiness, monitoring, and feedback mechanisms that enable teams to assess whether solutions are delivering intended outcomes over time. Guides teams and stakeholders through tradeoffs involving confidence, limitations, fairness, generalizability, and business risk. Creates repeatable evaluation practices that can be applied across engagements to improve consistency, comparability, and decision quality. Presents findings and recommendations to senior customer and Microsoft stakeholders with clarity on impact, uncertainty, and next steps.
Serves as a recognized technical and domain leader who brings together customer signals, delivery experience, market trends, and advances in AI/data science to shape strategy. Identifies opportunities to create new value across customers, industries, and solution areas by translating emerging needs into reusable approaches, offerings, and delivery priorities. Influences engineering and architecture direction by highlighting patterns, gaps, and opportunities observed across engagements. Creates durable intellectual property such as playbooks, reference architectures, evaluation approaches, and best practices that improve delivery quality at scale. Represents Microsoft through executive customer conversations, conferences, white papers, blog posts, and other thought leadership forums. Drives collaboration across teams to increase reuse, accelerate innovation, and strengthen Microsoft’s point of view in data science and AI delivery.
Provides principal-level technical leadership in code quality, maintainability, production readiness, and debugging practices for advanced analytics and machine learning systems. Goes deep hands-on when needed to resolve high-risk technical issues, validate architectural choices, or unblock critical delivery milestones. Establishes and promotes engineering patterns for readable, extensible, well-tested code and reliable operationalization across multiple teams and solutions. Guides teams on effective debugging, defect prevention, observability, and root-cause analysis for data and model pipelines. Defines expectations for deployment documentation, knowledge transfer, and operational support so solutions remain understandable and sustainable after delivery. Leverages technical proficiency in scalable engineering and MLOps concepts such as Apache Spark, CI/CD, Docker, Delta Lake, MLflow, Azure Machine Learning, and REST API development and consumption, while helping teams apply these capabilities in ways that improve reuse and long-term supportability.
Partners with customers and Microsoft cross-functional stakeholders to define strategic roadmaps for data science and AI solutions that span multiple initiatives and create measurable business value over time. Influences prioritization, sequencing, and tradeoff decisions by connecting technical choices to business outcomes, delivery risk, and long-term capability needs. Drives adoption of common patterns, governance expectations, and success measures that improve execution across teams. Uses storytelling, visualizations, and principled argumentation to align stakeholders and secure support for high-impact decisions. Reinforces and scales standards related to responsible AI, privacy, bias, and ethics across engagements. Helps capture and operationalize delivery learnings so they become reusable assets for future work.
Acts as a trusted advisor to customer and Microsoft stakeholders by combining technical depth, business judgment, and clear communication. Builds credibility with senior leaders by helping them understand where data science can create value, what constraints must be addressed, and which tradeoffs matter most. Navigates complex stakeholder environments to align technical, business, and delivery perspectives around practical paths forward. Drives customer adoption by shaping solutions that are interpretable, supportable, and matched to organizational needs rather than only technical ambition. Builds durable trust through transparency about data limitations, model risks, and operational realities. Helps customers make capability decisions that strengthen long-term success, not just immediate project outcomes.
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