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: 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.

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.

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 logisticregression, k-means, gradient boosting, autoregressive integrated movingaverage [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 andmachine 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.

Evaluation: 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. 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.

Business Management
Collaborates with end customer and Microsoftinternal 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.

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.

Others: Embody our culture and values

Qualifications

Required/Minimum Qualifications

Bachelor's/ Masters degree 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 Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.

Experience of deploying data science solutions in production on public cloud.

Experience of working with generative AI (Not Pilots/POCs)

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.

Senior Data Scientist