Data Science

Multiple Locations, Canada

Date posted

Aug 27, 2025

Job number

1858398

Work site

Up to 100% work from home

Travel

0-25**%**

Role type

Individual Contributor

Profession

Research, Applied, & Data Sciences

Discipline

Data Science

Employment type

Full-Time

Overview

With more than 45,000 employees and partners worldwide, the Customer Experience and Success (CE&S) organization is on a mission to empower customers to accelerate business value through differentiated customer experiences that leverage Microsoft’s products and services, ignited by our people and culture. We drive cross-company alignment and execution, ensuring that we consistently exceed customers’ expectations in every interaction, whether in-product, digital, or human-centered. CE&S is responsible for all up services across the company, including consulting, customer success, and support across Microsoft’s portfolio of solutions and products. Join CE&S and help us accelerate AI transformation for our customers and the world.

Within CE&S, the Customer Service & Support (CSS) organization builds trust and confidence for every person and organization through delivering a seamless support experience. In CSS, we are powered by Microsoft’s AI technology to help consumers, businesses, partners, and more, resolve their issues quickly and securely, helping prevent future problems from occurring and achieving more from their Microsoft investment.

We are seeking a Data Scientist with deep expertise in statistical modeling and machine learning to advance our understanding of support case dynamics and operational efficiency. This role will focus on developing AI/ML algorithms to assess case complexity, model labor time within support workflows, and uncover insights that drive measurable improvements in throughput and resource utilization. The ideal candidate will bring a strong foundation in regression analysis, causal inference, and experimentation, contributing to a data science practice that is central to operational excellence and transformation across CSS.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Qualifications

Required Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) 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 3+ 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 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred or additional Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ 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 5+ 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 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Data Science IC4 - The typical base pay range for this role across Canada is CAD $114,400 - CAD $203,900 per year.

Find additional pay information here:

https://careers.microsoft.com/v2/global/en/canada-pay-information.html

Microsoft will accept applications for the role until Sept 3rd, 2025.

#CES #CSS

Responsibilities

Application of AI (AOAI) & Efficiency Measurement:

• Develop and implement causal inference models and statistical frameworks to quantify labor effort within support cases—including active engineer time, idle time, and wait time. Extend these models to evaluate the impact of AI-driven systems (e.g., AOAI agents) on case resolution efficiency, agentic throughput, and cost avoidance. Leverage telemetry, case metadata, and experimental design to isolate AI contributions and optimize support workflows.

Productivity and Throughput Analytics**:**

• Build predictive models and dashboards to analyze throughput, case complexity, and routing efficiency. Apply statistical and machine learning techniques to surface process defects, blind transfers, and excessive case handoffs.

Agentic**& Autonomous System Evaluation:**

• Collaborate with engineering and supportability teams to evaluate autonomous agent performance. Develop capacity models and performance metrics to assess agentic build-out efficiency and ROI.
• 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:

• Understands problems facing projects and is able to leverage knowledge of data science to be able to uncover important factors that can influence outcomes on specific products. Describes the primary objectives of the team from a business perspective. 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 standards and best practices. Uses his or her understanding of organizational dynamics, interrelationships among teams, schedule constraints, and resource constraints to effectively influence partners to take action on insights. 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.

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 and communicates to senior leads. Develops useable data sets for modeling purposes. Contributes to ethics and privacy policies related to collecting and preparing data by providing updates and suggestions around internal best practices. Contributes to data integrity/cleanliness conversations with customers.

Scientific Rigor in Modeling:

• advanced statistical methods (e.g., logistic regression, time series analysis, survival modeling) to support hypothesis-driven experimentation and causal inference in operational workflows. Prioritize model interpretability and reproducibility.
• Understands relationship between selected models and business objectives. Ensures clear linkage between selected models and desired business objectives. Assesses the degree to which models meet business objectives. Defines and designs feedback and evaluation methods. Coaches and mentors less experienced engineers as needed. Presents results and findings to senior customer 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 machine learning solutions (e.g., classification, regression, clustering, forecasting, NLP, image recognition, etc.) and individual algorithms (e.g., linear and logistic regression, k-means, gradient boosting, autoregressive integrated moving average [ARIMA], recurrent neutral networks [RNN], long short-term memory [LSTM] networks) to identify the best approach to complete objectives. Understands modeling techniques (e.g., dimensionality reduction, cross validation, regularization, encoding, assembling, activation functions) and selects 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 data 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. Helps the Solution Architect and 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.

KPI and Metric Governance Support:

• Partner with business insights and governance teams to rationalize CSS measurement methodologies on core KPIs, prioritize high-impact metrics, and support development for MBR and executive reviews

Other:

• Embody our culture and values

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

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Industry leading healthcare

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Educational resources

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Discounts on products and services

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Savings and investments

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Maternity and paternity leave

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Generous time away

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Giving programs

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Opportunities to network and connect

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 and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.