Principal Data Scientist

Overview

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.

Responsibilities

Business Understanding and Impact

* 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, work streams, or long-term platform choices.

Assesses resources, dependencies, risks, assumptions, and constraints across multiple work streams 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.

Data Preparation and Understanding

* 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.

Modeling and Statistical Analysis

* 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…