Principal Data Scientist

Microsoft

About The Position

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.

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.
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.
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, including understanding and maintaining scalable data pipelines and machine learning models.
10+ years of hands-on experience translating business requirements into data-driven solutions using ML algorithms (e.g., classification, regression, clustering, NLP etc.).
Strong communication skills and ability to collaborate across cross-functional teams.
Experience managing stakeholder and leader communications effectively.
Proven ability to mentor junior data scientists and lead end-to-end ML lifecycle projects.

Nice To Haves

2+ year of experience in PowerBI reporting and SSAS is a plus
2+ year of experience in business planning is plus.
Experience in quota modeling, incentive compensation, or sales analytics and forecast is a plus.
Hands-on experience with cloud platforms and tools such as Azure Synapse and Azure Foundry, with a focus on developing and deploying AI models is a plus.
Experience designing, building, or deploying agentic AI systems — including autonomous agents, multi-agent orchestration, tool-use frameworks, or agent-based workflows using platforms such as LangChain, AutoGen, Semantic Kernel, or similar is a plus.

Responsibilities

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 — ensuring transparency and building trust in the quota-setting process.
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, and maintains strong expertise in modeling, coding, and debugging techniques — including isolating and resolving errors and defects.
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 and explores potential for new capabilities.
Brings expert-level proficiency in big-data and ML engineering tools and practices, including Hadoop, Apache Spark, CI/CD, Docker, Delta Lake, MLflow, Azure ML, and REST API development.
Maintains a customer-first mindset — understanding stakeholder needs, validating their perspectives, and serving as a trusted advisor within the broader organizational context.
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 — modules, packages, and general-purpose tools — for broader team reuse. Enforces team standards for bias, privacy, and ethics. Reviews teammates' model methodology and performance, recommending improvements where appropriate.
Anticipates risks such as data leakage, bias/variance tradeoffs, and methodological limitations, guiding teammates toward sound solutions. 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 — analyzing distributions, attribute relationships, sub-population properties, and statistical summaries.
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, highlighting gaps or areas needing reexamination. Uses assessment findings to determine next steps — deployment, further iteration, or new project directions.
Ensures clear alignment between selected models and business objectives, validating that model outputs drive meaningful outcomes.
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, including third-party data. Champions ethics and privacy discussions, integrating industry-wide insights to influence internal processes and decision-making.
Maintains strong proficiency in the Microsoft AI/ML toolset (Azure Machine Learning, Azure Cognitive Services, Azure Databricks). Translates complex statistical and ML concepts into accessible explanations for customers and stakeholders.
Embody our culture and values.

Benefits

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

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