Job 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

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.