Principal Data Scientist Job at Microsoft Corporation (Multiple Locations)

Principal Data Scientist

Microsoft Corporation

Location:
United States , Multiple Locations

Category:
IT - Software Development

Contract Type:
Employment contract

Salary:

142800.00 - 304200.00 USD / Year

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Job Description:

Microsoft is a company where passionate innovators come to collaborate, envision what can be, and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit of thinking in a cloud-enabled world. We are looking for a Principal Data Scientist who is willing to work in a dynamic environment to solve real life day-to-day problems, leveraging data science techniques. You will enjoy and be successful in this role if you are curious and willing to challenge the status quo and come up with data-driven solutions to ambiguous problems. As a Principal Data Scientist, you will partner closely with data engineering, product, field, and Finance teams to turn large‑scale telemetry into decision-ready insights. You will help define compensable metrics, design quota models, evaluate outcomes, and ensure our quota distribution is explainable, reliable, and aligned to real business questions. Your work will directly influence product direction, customer success motions, and executive decision‑making. Microsoft’s mission is to empower every person and every organization on the planet to achieve more, and we’re dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their highly qualified contributions each day. Join us and help shape the future of the world.

Job Responsibility:

Defines quota-setting strategy aligned with business, customer, and solution objectives
Partners cross-functionally to identify and pursue opportunities for applying machine learning and other data-science methods to quota and incentive design
Bridges Finance, Sales, Business Sales Operations, and Product teams through deep technical expertise
Drives cross-discipline collaboration and leads efforts to refine intellectual property definitions and methodology improvements
Educates field managers and sales leaders on quota methodology, data inputs, and model mechanics through roadshows, workshops, and ongoing enablement
Applies deep domain expertise to analyze challenges across product lines, identifying and mitigating risks that could influence quota outcomes
Partners with business stakeholders to shape strategy, recommend improvements, and surface opportunities to extend existing work into new contexts
Establishes and promotes standards and best practices across teams
Writes efficient, readable, and extensible code and models spanning multiple features and solutions
Contributes to code and model reviews with actionable feedback
Maintains strong expertise in modeling, coding, and debugging techniques
Leads project teams in gathering, integrating, and interpreting data from multiple sources to troubleshoot issues end-to-end
Provides feedback to product groups on non-optimized features
Brings expert-level proficiency in big-data and ML engineering tools and practices
Maintains a customer-first mindset
Adds strategic value by connecting business understanding, product functionality, data sources, and methodology expertise to reframe problems and deliver actionable insights
Leads customer discussions and offers pragmatic solutions that account for real-world data limitations
Generalizes ML solutions into repeatable frameworks
Enforces team standards for bias, privacy, and ethics
Reviews teammates' model methodology and performance
Anticipates risks such as data leakage, bias/variance tradeoffs, and methodological limitations
Drives best practices in model validation, implementation, and deployment
Develops operational models that run reliably at scale
Partners cross-functionally to identify opportunities for ML and predictive analysis
Uncovers new customer scenarios for transformative ML-driven solutions while incorporating AI ethics best practices
Maintains deep, current expertise in emerging AI/ML methodologies
Oversees data acquisition and ensures datasets are properly formatted and accurately documented
Uses SQL, Python, and visualization tools to explore data
Builds data platforms from scratch across product lines
Designs data-science business solutions using established technologies, patterns, and practices
Provides guidance on operationalizing models created by data scientists
Identifies new opportunities from data and processes it for general-purpose use
Contributes to thought leadership and IP on data acquisition best practices
Leads resolution of data-integrity issues
Conducts thorough reviews of analytical techniques and processes
Uses assessment findings to determine next steps
Ensures clear alignment between selected models and business objectives
Defines and designs feedback loops and evaluation methods to measure ongoing model impact
Mentors engineers on data cleaning, analysis best practices, and ethical data handling
Identifies gaps in existing datasets and drives onboarding of new sources
Champions ethics and privacy discussions
Maintains strong proficiency in the Microsoft AI/ML toolset
Translates complex statistical and ML concepts into accessible explanations for customers and stakeholders
Embody our culture and values

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 have:

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 12+ years data-science experience
OR equivalent experience
10+ years of hands-on experience with cloud data platforms (e.g., Azure, AWS or Google etc.)
10+ years of programming experience in Python, SQL Server, and PySpark
10+ years of hands-on experience translating business requirements into data-driven solutions using ML algorithms (e.g., classification, regression, clustering, NLP etc.)
2+ year of experience in PowerBI reporting and SSAS is a plus
2+ year of experience in business planning is plus
Strong communication skills
Experience managing stakeholder and leader communications effectively
Experience in quota modeling, incentive compensation, or sales analytics and forecast is a plus
Proven ability to mentor junior data scientists
Hands-on experience with cloud platforms and tools such as Azure Synapse and Azure Foundry is a plus
Experience designing, building, or deploying agentic AI systems is a plus