Principal Data Scientist

OverviewIn the Microsoft Frontier Company Engineering team, you won’t just build software; you’ll deliver real outcomes for some of the world’s most complex organizations. We are a global team of world-class engineers who have been working in a Forward Deployed Engineering (FDE) model for more than a decade, embedding directly within customer teams to solve their toughest challenges, side-by-side, and shipping production-ready solutions in days, not <months.As> a Data Scientist, you will help transform how organizations use data, developing models, running experiments, and driving evidence-based decisions that have measurable business impact. You’ll work in small, multidisciplinary crews alongside engineers, product managers, and domain experts, owning the full lifecycle from data discovery through to deployed solutions.Our work is powered by AI, applying the mindset and tooling across the entire development lifecycle to move from idea to production in days while maintaining the highest standards of security, quality, and trust.Responsibilities​Business Understanding and Impact:Leverages subject matter expertise to analyzeproblems and issues facing projects to uncover, manage, and/or mitigate factorsthat can influence final outcomes across product lines. Partners with businessteam to drive strategy and recommend improvements. Raises opportunities to lookfor new work opportunities and different contexts to use existing work.Establishes, applies, and teaches standards and best <practices.Data> Preparation and Understanding:Oversees data acquisition efforts and ensuresdata is properly formatted and accurately described. Utilizes key technologiesand tools necessary for data exploration (e.g., structured query language[SQL], Python). Uses querying, visualization, and reporting techniques toexplore the data, including distribution of key attributes, relationships betweenattributes, simple aggregations, properties of significant sub-populations, andstatistical analyses. Mentors and coaches engineers in data cleaning andanalysis best practices. Identifies gaps in current data sets and drivesonboarding of new data sets (e.g., bringing on third-party data sets). Drivesdiscussions around ethics and privacy policies related to collecting andpreparing data. Integrates industry-wide ethics insights and best practices toinfluence internal processes and drive decision-making. Builds data platformsfrom scratch across products Builds data-science business solutions usingexisting technologies, products, and solutions, as well as established patternsand practices. Provides guidance on model operationalization of models createdby data scientists. Identifies new opportunities from data and processes datain a way that is usable for general purpose. Actively contributes to the bodyof thought leadership and intellectual property (IP) on best practices for dataacquisition and understanding. Leads and resolves data-integrity problems.Modeling and Statistical Analysis:Generalizes machine learning (ML) solutionsinto 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. Anticipatesthe risks of data leakage, the bias/variance tradeoff, methodologicallimitations, etc., and is able to guide teammates on solutions. Drives bestpractices relative to model validation, implementation, and application.Develops operational models that run at scale. Partners with others to identifyand explore opportunities for the application of ML and predictive analysis.Identifies new customer opportunities for driving transformative customersolutions with ML modeling. Incorporates best practices for ML modeling withconsideration for artificial intelligence (AI) ethics. Develops deep expertisein specialized areas by staying abreast of current and emerging methodologiesan AI and ML.Evaluation:Conducts thorough review of data analysis andmodeling techniques used to summarize the process review and highlight areas thathave been missed or need reexamining. Utilizes results of the assessment andprocess review to decide on next steps (e.g., deployment, further iterations,new projects). Identifies new evaluation approaches and metrics and invents newmethodologies to evaluate models.Industry and Research Knowledge/Opportunity Identification:Tracks advances in industry and academia,identifies relevant state-of-the-art research, and adapts algorithms and/ortechniques to drive innovation and develop new solutions. Researches andmaintains deep knowledge of industry trends, technologies, and advances.Leverages knowledge of work being done on team to propose collaborationefforts. Proactively develops strategic responses to specific market strengths,weaknesses, opportunities, threats, and/or trends. Mentors and coaches lessexperienced engineers in data analysis best practices. Serves as subject matterexpert and role model for less experienced engineers. Identifies strategyopportunities. Actively contributes to the body of thought leadership andintellectual property (IP) best practices by actively participating in externalconferences.Coding and Debugging:Independently writes efficient, readable,extensible code/model that spans multiple features/solutions. Contributes tothe code/model review process by providing feedback and suggestions forimplementation and improvement. Develops expertise in proper modeling, coding,and/or debugging techniques such as locating, isolating, and resolving errorsand/or defects. Leads a project team in the gathering, integrating, andinterpreting of data/information from multiple sources in order to properlytroubleshoot errors. Provides feedback on non-optimized features/solutions backto product group, and explores potential for new features. Leveragesexpert-level proficiency of big-data software engineering concepts, such asHadoop 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:Defines business-strategy goals,customer-strategy goals, and solution-strategy goals. Partners with teams toidentify and explore opportunities for the application of machine learning (ML)and other data-science tools. Leverages technical expertise to developpartnerships between product teams, Sales teams, Area teams, and Services. Workcollaboratively across disciplines. Leads involvement of intellectual property (IP)definition improvement. Coaches and mentors less experienced engineers.Customer/Partner Orientation:Commits to a customer-oriented focus byacknowledging customer needs and perspectives, validating customerperspectives, focusing on broader customer organization/context, and serving asa trusted advisor. Identifies opportunities and adds valuable insight byincorporating 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 thecustomer. Leads the discussion with customers and offers pragmatic solutionsthat are feasible given their data limitations.Other:Embody our culture and valuesQualifications​Required/Minimum QualificationsBachelor's degree in data science,Mathematics, Statistics, Econometrics, Economics, Operations Research, ComputerScience, or related field AND 10+ years data-science experience (e.g., managingstructured and unstructured data, applying statistical techniques and reportingresults)OR ​master's degree in data science,Mathematics, Statistics, Econometrics, Economics, Operations Research, ComputerScience, or related field AND 7+ years data-science experience (e.g., managingstructured and unstructured data, applying statistical techniques and reportingresults)OR Doctorate in Data Science, Mathematics,Statistics, Econometrics, Economics, Operations Research, Computer Science, orrelated field AND 5+ years data-science experience (e.g., managing structured andunstructured data, applying statistical techniques and reporting results)OR equivalent experience.Business-level or higher proficiency in both Japanese and English is required. Additional or Preferred Qualifications:Bachelor's degree in data science,Mathematics, Statistics, Econometrics, Economics, Operations Research, ComputerScience, or related field AND 12+ years data-science experience (e.g., managingstructured and unstructured data, applying statistical techniques and reportingresults)OR master's degree in data science, Mathematics, Statistics,Econometrics, Economics, Operations Research, Computer Science, or relatedfield AND 10+ years data-science experience (e.g., managing structured andunstructured data, applying statistical techniques and reporting results)OR Doctorate in Data Science, Mathematics, Statistics,Econometrics, Economics, Operations Research, Computer Science, or relatedfield AND 8+ years data-science experience (e.g., managing structured andunstructured data, applying statistical techniques and reporting results)ORequivalent 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.