Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Principal Data Scientist - Microsoft 365 Copilot

Microsoft
Greater London
5 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Principal Data Scientist, AI Security Research

Principal Data Scientist- CPG

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist

Overview

Microsoft's mission is to empower every person and every organization on the planet to achieve more. Microsoft believes that artificial intelligence will play a critical role in accomplishing that mission.


We’re looking for a Principal Data Scientist to help shape the future of Microsoft 365 Copilot, with a focus on Search, Chat and Research experiences. This role sits within the Microsoft Search, Assistant, and Intelligence (MSAI) organization, which powers the intelligence behind M365 Copilot by combining cutting-edge advances in generative AI with personalized search and recommendation systems. As a Principal Scientist in MSAI, you will work in an exciting and fast-paced, collaborative environment.

You’ll bring deep expertise in large language models (LLMs), information retrieval, and machine learning to improve the quality and scalability of M365 Copilot. You’ll partner closely with engineering and product teams to innovate, design and evaluate end-to-end AI solutions that serve millions of enterprise users. This is a high-impact role where you’ll influence technical strategy, shape product direction, and collaborate across Microsoft to deliver AI-powered experiences that help users accomplish more with less effort.

Qualifications

Required Qualifications:

A Ph.D. in Computer Science, Math, Physics, Statistics, OR related areas is highly preferred. Candidates with master’s degree with proven industry experience or a strong publication record in the areas of Information Retrieval, Machine Learning, Natural Language Processing, and Deep Learning are considered as well. Extensive hands-on experience building and deploying products using Machine Learning. Specifically, we are looking for expertise in Natural Language Processing, Large Language Models, Information Retrieval, and Recommendation Systems with a good understanding of techniques like Differential Privacy, Responsible AI and related areas.  High proficiency in deploying machine learning applications at scale in real production environments and proven track record of successfully shipping applied research to production is a must  Excellent problem solving and data analysis skills and a good grasp of applied statistics. Particularly, expertise in developing or applying predictive analytics, statistical modelling, data mining, or machine learning algorithms, especially at scale  Strong people leadership skills to influence others, with the ability to tech-lead, understand team dynamics, retain, attract, and develop team members.  Grounded in growth mindset, and advocate for diversity and inclusion.  Customer obsession and passionate about product impact  Excellent verbal and written communication skills, with the ability to simplify and explain complex ideas.  Effective collaboration skills while working effectively within a globally distributed organization. 


Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

PhD degree in Computer Science, Statistics, Mathematics, OR a related field. Proven track record in training large language models and post-training large language models, using reinforcement learning or similar techniques First-hand experience building agentic AI models using deep learning

#M365CORE

Responsibilities

You’ll work on high-impact, technically ambitious projects that directly shape the future of Microsoft 365 Copilot. Examples include:Advancing deep reasoning in Microsoft 365 Copilot by applying next-generation LLM fine-tuning and reinforcement learning techniques.

Improving Copilot Chat and Researcher response quality through state-of-the-art grounding data selection strategies.

Enhancing Copilot Search by developing novel content representation models.

Building the next wave of recommendation and personalization capabilities across M365 Copilot experiences.

In addition to driving innovation, you’ll help grow the team’s technical depth by mentoring and developing talent.Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.Industry leading healthcareEducational resourcesDiscounts on products and servicesSavings and investmentsMaternity and paternity leaveGenerous time awayGiving programsOpportunities to network and connect

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.