Principal Data Scientist

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.

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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.