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About the Role

As a Data Scientist in the Microsoft Frontier Company Engineering team, you will transform how organizations use data by developing models, running experiments, and driving evidence-based decisions with measurable business impact. You will work in multidisciplinary crews, owning the full lifecycle from data discovery to deployed solutions, powered by AI and adhering to high standards of security, quality, and trust.

Responsibilities

Leverages subject matter expertise to analyze problems and issues facing projects, managing factors that influence outcomes across product lines.
Partners with business teams to drive strategy and recommend improvements, identifying new work opportunities.
Establishes, applies, and teaches standards and best practices.
Oversees data acquisition efforts, ensuring data is properly formatted and accurately described.
Utilizes querying, visualization, and reporting techniques to explore data, including distributions, relationships, aggregations, and statistical analyses.
Mentors and coaches engineers in data cleaning and analysis best practices.
Identifies gaps in current data sets and drives the onboarding of new data sets.
Drives discussions around ethics and privacy policies related to data collection and preparation.
Integrates industry-wide ethics insights and best practices to influence internal processes.
Builds data platforms and data-science business solutions using existing technologies and established patterns.
Provides guidance on the operationalization of models created by data scientists.
Identifies new opportunities from data and processes data for general use.
Contributes to thought leadership and intellectual property on data acquisition and understanding best practices.
Leads and resolves data-integrity problems.
Generalizes machine learning (ML) solutions into repeatable frameworks for others to use.
Enforces team standards related to bias, privacy, and ethics.
Evaluates the methodology and performance of teammates’ models, recommending improvements.
Anticipates risks such as data leakage and bias/variance tradeoff, guiding teammates on solutions.
Drives best practices for model validation, implementation, and application.
Develops operational models that run at scale.
Partners with others to identify and explore opportunities for ML and predictive analysis.
Identifies new customer opportunities for transformative solutions with ML modeling.
Incorporates best practices for ML modeling with consideration for AI ethics.
Develops deep expertise in specialized areas by staying abreast of current and emerging AI and ML methodologies.
Conducts thorough reviews of data analysis and modeling techniques to summarize processes and highlight areas needing reexamination.
Utilizes assessment results to decide on next steps, such as deployment or further iterations.
Identifies new evaluation approaches and metrics, inventing new methodologies to evaluate models.
Tracks advances in industry and academia, adapting algorithms and techniques to drive innovation.
Researches and maintains deep knowledge of industry trends, technologies, and advances.
Leverages knowledge of team's work to propose collaboration efforts.
Proactively develops strategic responses to market strengths, weaknesses, opportunities, threats, and trends.
Serves as a subject matter expert and role model for less experienced engineers.
Identifies strategy opportunities.
Actively contributes to thought leadership and intellectual property best practices by participating in external conferences.
Independently writes efficient, readable, extensible code/models spanning multiple features/solutions.
Contributes to the code/model review process by providing feedback and suggestions.
Develops expertise in proper modeling, coding, and debugging techniques.
Leads project teams in gathering, integrating, and interpreting data from multiple sources to troubleshoot errors.
Provides feedback on non-optimized features/solutions to product groups and explores potential new features.
Leverages expert-level proficiency in big-data software engineering concepts (e.g., Hadoop Ecosystem, Apache Spark, CI/CD, Docker, Delta Lake, MLflow, AML, REST API).
Defines business-strategy, customer-strategy, and solution-strategy goals.
Leverages technical expertise to develop partnerships between product teams, Sales teams, Area teams, and Services.
Works collaboratively across disciplines.
Leads involvement in intellectual property definition improvement.
Commits to a customer-oriented focus by acknowledging customer needs and perspectives.
Identifies opportunities and adds valuable insight by incorporating understanding of business, product functionality, data sources, methodologies, and customer perspective.
Interprets results, develops insights, and effectively communicates them to the customer.
Leads discussions with customers and offers pragmatic solutions considering data limitations.
Embody our culture and values.

Requirements

Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience OR master's degree AND 7+ years data-science experience OR Doctorate AND 5+ years data-science experience OR equivalent experience.
Business-level or higher proficiency in both Japanese and English is required.
Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 12+ years data-science experience OR master's degree AND 10+ years data-science experience OR Doctorate AND 8+ years data-science experience OR equivalent experience.

Skills

Data Science
Machine Learning (ML)
Artificial Intelligence (AI)
SQL
Python
Data Preparation
Data Exploration
Statistical Analysis
Modeling
Model Evaluation
Coding
Debugging
Big Data Software Engineering
Hadoop Ecosystem
Apache Spark
Continuous Integration and Continuous Delivery (CI/CD)
Docker
Delta Lake
MLflow
AML
REST API

Experience Level

10+ years data-science experience
7+ years data-science experience
5+ years data-science experience
12+ years data-science experience
8+ years data-science experience

Education Level

Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
Master's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field

About the Company

Microsoft Frontier Company Engineering team delivers real outcomes for complex organizations.
We are a global team of world-class engineers using a Forward Deployed Engineering (FDE) model.
We embed directly within customer teams to solve their toughest challenges and ship production-ready solutions quickly.

Equal Opportunity

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.