Role overview
Are you a skilled Data Scientist with a passion for AI? Would you like to build insights, models, and experiments that help customers consistently realize value from Microsoft 365 Copilot?
The Data Science team in Microsoft Ireland partners closely with product managers and engineers to drive growth in active usage of Microsoft 365 Copilot across home and business customers. Through deep data analysis, modelling, and large‑scale experimentation, we study how customers discover value in Copilot, which experiences drive sustained engagement, and where product improvements unlock meaningful productivity gains. By turning complex usage and experimentation data into clear, actionable insights, our work directly informs product decisions and accelerates Copilot adoption at scale.
In this role, you will help advance data science and AI innovation that shapes the future of Microsoft 365 Copilot. You will translate open‑ended business and customer questions into analytical, machine‑learning, and AI‑driven solutions by designing experiments, developing scorecards, and rigorously interpreting results. You will work hands‑on with modern AI technologies—including large language models and experimentation frameworks—and write production‑quality code that integrates into Azure‑based engineering systems. By applying strong research thinking, responsible AI practices, and scalable data science techniques, you will help evolve Copilot experiences and ensure the product delivers measurable value at scale. This is a hybrid role, involving working from the office three or more days per week. See what its like to work at Microsoft Ireland in our European Development Centre
Responsibilities
- Collaborate with cross‑functional partners to understand customer and product goals and contribute to growth in Microsoft 365 Copilot through the application of best practices in data science.
- Translate business and customer problems into analytical, machine learning, causal modeling, and AI‑driven solutions by selecting and applying appropriate methodologies in Python and SQL.
- Design, execute, and analyse A/B experiments by forming hypotheses, building scorecards, calculating new metrics, and interpreting results to inform product decisions.
- Write efficient, readable, and maintainable production‑quality code and collaborate with engineering partners to integrate data models and analyses into Azure‑based systems.
- Engage in AI research and development activities involving LLM fundamentals, prompt engineering, evaluation of LLM output, agents, and construction of LLM powered applications.
- Evaluate model and analysis performance against business objectives by testing on real or production data, incorporating stakeholder feedback, and contributing to reviews of assumptions, risks, and limitations.
- Learn and apply current data science, AI, privacy, security, and compliance best practices; engage with internal research and senior peers to share knowledge and contribute to scalable, responsible data‑driven solutions across Microsoft.
- Do you have a degree (Bachelor’s, Master’s, or Doctorate) in a relevant quantitative field, or equivalent experience, along with the required level of data science experience?
- Extensive experience with coding in SQL and R/Python/Spark to implement statistical models, machine learning, and analysis on big data
- Deep knowledge of LLM/GPT fundamentals, and experience with prompt engineering, evaluation of LLM output, and agents
- Outstanding written and oral communication, exemplified through experience in collaborative problem-solving, and presenting findings to technical and non-technical audiences
- Extensive experience translating research or business problems into analytical, machine learning, and AI‑driven solutions
Preferred qualifications
- PhD or Research-based Master’s Degree in Data Science or Computer Science, Statistics, with substantial data science experience
- Substantial experience with A/B experimentation, including in the design and application of experiments, analysis of results, and development of tools to enable experimentation at scale
- Deep experience with designing and deploying systems and services powered by AI infrastructure, with emphasis on scalability, reliability, and performance in production environments.