Principal Data Scientist

OverviewDo you enjoy viewing problems from different perspectives to find the best solutions? Do you thrive on working closely with customers to innovate and solve complex challenges? Are you excited by the opportunity to identify trends and deliver unique business solutions? Do you want to join a team where learning about new technologies and solutions is part of our daily routine? The Industry Solutions Delivery (ISD) Engineering & Architecture Group (EAG) is a global consulting and engineering organization that supports ourmost complex and leading-edgecustomer engagements. Driving early-stage deliveries, partnering withothers to develop new approaches and innovative solutions, and implementing them using state-of-the-art engineeringstandards to set our sales and delivery teams up forsuccess. We provide consistent high-quality customerexperience throughtechnical leadership for AI and IP capture centered on delivery truth. As part of the team, you will be a key leader in the largest Data Science and AI team in the Industry Solutions Organization, you will learn in a fast paced, production focused environment, delivering customer value with everything we do and help protect Microsoft's enterprise customers. The job provides an opportunity to:Impact one of the fastest growing teams in Industry Solutions Delivery that is critical to Microsoft's AI strategy.Work in a world class team of Data Scientist, AI Engineers, Data Engineers, Architects, and leadership that will help you grow your career.Be part of a dynamic AI community that will enable you to learn, collaborate, and contribute with the top minds in the industry.We are looking for someone who is highly customer focused with the right combination of curiosity, technical aptitude, and communication skills to become a Principal Data Scientist in the EAG Data Science Engineering team within the Industry Solutions Delivery organization. You will be part of a high-performing AI Engineering organization and will be in a role that is focused on customer success and satisfaction. Since we are an AI Engineering team, we focus a good deal on Data Science and AI technologies, so we're seeking candidates with a track record of addressing complex customer scenarios in the Data & AI solution space. What's also super important is that you can show empathy for customers, their business outcomes, and plans, and are proficient at guiding teamwork to deliver great AI outcomes for our customers. We are a team of fun, dynamic, supportive community and our Leadership is committed to delivering the best AI solutions and services to our customers. We get to develop and run innovative Data & AI services at extremely large scale for our enterprise customers, which presents challenges we love to solve.ResponsibilitiesBusiness Understanding and ImpactLeverages subject matter expertise to analyze problems and issues facing projects to uncover, manage, and/or mitigate factors that can influence final outcomes across product lines. Partners with business team to drive strategy and recommend improvements. Raises opportunities to look for new work opportunities and different contexts to use existing work. Establishes, applies, and teaches standards and best practices.Data Preparation and UnderstandingOversees data acquisition efforts and ensures data is properly formatted and accurately described. Utilizes key technologies and tools necessary for data exploration (e.g., structured query language [SQL], Python). Uses querying, visualization, and reporting techniques to explore the data, including distribution of key attributes, relationships between attributes, simple aggregations, properties of significant sub-populations, and statistical analyses. Mentors and coaches engineers in data cleaning and analysis best practices. Identifies gaps in current data sets and drives onboarding of new data sets (e.g., bringing on third-party data sets). Drives discussions around ethics and privacy policies related to collecting and preparing data. Integrates industry-wide ethics insights and best practices to influence internal processes and drive decision-making. Builds data platforms from scratch across products Builds data-science business solutions using existing technologies, products, and solutions, as well as established patterns and practices. Provides guidance on model operationalization of models created by data scientists. Identifies new opportunities from data and processes data in a way that is usable for general purpose. Actively contributes to the body of thought leadership and intellectual property (IP) on best practices for data acquisition and understanding. Leads and resolves data-integrity problems.Modeling and Statistical AnalysisGeneralizes machine learning (ML) solutions into repeatable frameworks (e.g., modules, packages, general-purpose software) for others to use. Exemplifies and enforces team standards related to bias, privacy, and ethics. Evaluates the methodology and performance of teammates' models and, as appropriate, recommends solutions for improvement. Anticipates the risks of data leakage, the bias/variance tradeoff, methodological limitations, etc., and is able to guide teammates on solutions. Drives best practices relative to model validation, implementation, and application. Develops operational models that run at scale. Partners with others to identify and explore opportunities for the application of ML and predictive analysis. Identifies new customer opportunities for driving transformative customer solutions with ML modeling. Incorporates best practices for ML modeling with consideration for artificial intelligence (AI) ethics. Develops deep expertise in specialized areas by staying abreast of current and emerging methodologies an AI and ML.EvaluationConducts thorough review of data analysis and modeling techniques used to summarize the process review and highlight areas that have been missed or need reexamining. Utilizes results of the assessment and process review to decide on next steps (e.g., deployment, further iterations, new projects). Identifies new evaluation approaches and metrics and invents new methodologies to evaluate models.Industry and Research Knowledge/Opportunity IdentificationTracks advances in industry and academia, identifies relevant state-of-the-art research, and adapts algorithms and/or techniques to drive innovation and develop new solutions. Researches and maintains deep knowledge of industry trends, technologies, and advances. Leverages knowledge of work being done on team to propose collaboration efforts. Proactively develops strategic responses to specific market strengths, weaknesses, opportunities, threats, and/or trends. Mentors and coaches less experienced engineers in data analysis best practices. Serves as subject matter expert and role model for less experienced engineers. Identifies strategy opportunities. Actively contributes to the body of thought leadership and intellectual property (IP) best practices by actively participating in external conferences.Coding and DebuggingIndependently writes efficient, readable, extensible code/model that spans multiple features/solutions. Contributes to the code/model review process by providing feedback and suggestions for implementation and improvement. Develops expertise in proper modeling, coding, and/or debugging techniques such as locating, isolating, and resolving errors and/or defects. Leads a project team in the gathering, integrating, and interpreting of data/information from multiple sources in order to properly troubleshoot errors. Provides feedback on non-optimized features/solutions back to product group, and explores potential for new features. Leverages expert-level 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 ManagementDefines business-strategy goals, customer-strategy goals, and solution-strategy goals. Partners with teams to identify and explore opportunities for the application of machine learning (ML) and other data-science tools. Leverages technical expertise to develop partnerships between product teams, Sales teams, Area teams, and Services. Work collaboratively across disciplines. Leads involvement of intellectual property (IP) definition improvement. Coaches and mentors less experienced engineers.Customer/Partner OrientationCommits to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer organization/context, and serving as a trusted advisor. Identifies opportunities and adds valuable insight by incorporating an understanding of the business, product/service functionality, data sources, methodologies to reframe problems, and the customer perspective. Interprets results, develops insights, and effectively communicates results to the customer. Leads the discussion with customers and offers pragmatic solutions that are feasible given their data limitations.OtherEmbody our culture and values