Overview Do you enjoy solving problems, looking at problems through a different lens, and working closely with customers to innovate new solutions to complex problems? Do you jump with excitement at the opportunity to identify trends and provide unique business solutions? Do you want to join a team where learning about a new technology or solution is part of our work every day? The Industry Solutions Delivery (ISD) Engineering & Architecture Group (EAG) is a global consulting and engineering organization that supports our most complex and leading-edge customer engagements. Driving early-stage deliveries, enhances ISD’s technical capabilities, and partnering with others to develop approaches, innovative solutions, and engineering standards in order to set our sales and delivery teams up for success. Leveraging the principles of model, care, and coach, we provide consistent high-quality customer experience through technical and AI leadership and IP capture centered on delivery truth. Responsibilities ​Business Understanding and Impact - Leverages understanding of data science and business to examine a project and consider factors that can influence final outcomes within a technical area. Evaluates project plan for resources, risks, contingencies, requirements, assumptions, and constraints. Documents key business objectives. Effectively communicates business goals in analytical and technical terms. Consistently shares insights with stakeholders. Data Preparation and Understanding - Understands where to acquire data necessary for successful completion of the project plan. Utilizes querying, visualization, and reporting techniques to describe acquired data, including format, quantity, identities, and other surface properties. Explores data for key attributes and contributes to the development of data quality report describing results of the task, initial findings, and impact on the project. Collaborates with others to perform data-science experiments using established methodologies, statistics, optimization, and probability theory for general purpose software and statistical packages. Assesses different tools and techniques and selects the appropriate one. Serves as an effective partner in data preparation efforts to Solution Architects, Consultants, and Data Engineers. Adheres to Microsoft's privacy policy related to collecting and preparing data. Identifies data integrity problems. Modeling and Statistical Analysis - Leverages knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, natural language processing [NLP], image recognition) 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, and stability. Develops operational models that run at scale through partnership with data engineering teams. Evaluation - Understands linkage between achieved model and business objectives. Assists with testing models on test applications and on real data or production data. Analyzes model performance. Incorporates implicit and explicit customer feedback into model evaluation. Conducts review of data analysis and modeling techniques to determine factors that may have been overlooked or need to be reexamined. Contributes to the summary of the review process. Industry and Research Knowledge/Opportunity Identification - Learns and understands the current state of the industry, including knowledge of tools, techniques, strategies, and processes that can be utilized to improve process efficiency and performance. Maintains knowledge of current trends within the discipline. Attends internal research conferences and participates in on-hands training, when appropriate. Actively contributes to the body of thought leadership and intellectual property (IP) best practices. Coding and Debugging - Understands existing code to write efficient and readable code of their own for a specific feature, seeking guidance as needed. Collaborates with other engineering teams to develop, test, and implement changes to optimize code to improve efficiency, reliability, diagnosability, maintainability, and operability of systems. Develops working expertise in proper debugging techniques such as locating, isolating, and resolving errors and/or defects. Collaborates with other engineers/project team members to integrate data models into customers' engineering systems. Understands 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. Business Management - Develops understanding of data structures and their relationship to Microsoft's customer business. Observes senior engineers and learns best practices in identifying growth opportunities, understanding strategy goals, customer- and product-strategy goals, and exploring opportunities for machine learning (ML) application, seeking guidance when needed. Understands business goals of the customer, per engagement basis. Customer/Partner Orientation - Leverages understanding of data science and business to examine projects through a customer-oriented focus. Manages customer expectations regarding project/product progress and timeline. Takes responsibility to enhance customer excellence. Assists and learns from senior team members interpret results, develop insights, and communicate results to customers. Possesses basic understanding about model accuracies' dependency on data quality and able to articulate it in customer discussions. Other Embody our culture and values Qualifications ​Required/Minimum Qualifications - Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ 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 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience. - OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field - OR equivalent experience. Additional or Preferred Qualifications - 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 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 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 equivalent experience. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. 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 with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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• Own the research and modeling work underlying Junction’s highest-priority data science opportunities across diagnostics, clinical workflows, and AI-enabled product features • Define rigorous frameworks for measurement, experimentation, and causal evaluation so we can distinguish signal from noise and make decisions we can defend • Lead development of predictive models, segmentation approaches, risk or routing logic, and other statistical systems that directly inform product and business strategy • Build the analytical foundation behind customer-facing features — from model development through to validation and performance tracking • Partner with engineering and data engineering to ensure models and analytical systems can be put in production, are reliable, and useful in real workflows • Establish how Junction evaluates data-driven and AI-enabled features, including methodology, quality thresholds, monitoring, and performance review • Communicate complex technical findings clearly to technical and non-technical stakeholders, including tradeoffs, limitations, and implications for action