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
