Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience (e.g., managing struc
Additional or Preferred Qualifications: Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 12+ years data-
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
Full description
Overview
In 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 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 Understanding: Oversees 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
Principal Data Scientist at Microsoft (Japan, Tokyo-to, Tokyo) — aijobsearch
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 Analysis: Generalizes 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.
Evaluation: Conducts 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 Identification: Tracks 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 Debugging: Independently 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 Management: Defines 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 Orientation: Commits 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.
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 10+ 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 7+ 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 5+ years data-science experience (e.g., managing structured and unstructured 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, Computer Science, or related field AND 12+ 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 10+ 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 8+ years 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.