About the role
About Magentic At Magentic, we’re building AI systems that can autonomously run complex procurement and supply chain workflows for some of the world’s largest companies. We’re tackling a genuinely hard real-world problem, helping global manufacturing supply chains become more resilient in an increasingly unpredictable world. It’s a massive space with huge untapped potential for AI. We’re an early-stage company backed by Sequoia Capital, with a team bringing experience from OpenAI, Meta, Revolut, NASA and McKinsey & Company. We’re looking for a Data Scientist to help us apply LLMs and AI tooling to large-scale, messy, real-world datasets, solving operational problems where the answers aren’t obvious and the impact is very tangible.
The Role This is not a traditional analytics or engineering role. We’re looking for someone who enjoys working deeply with data, experimentation, AI tooling, and problem solving, someone comfortable using Python, notebooks, LLMs, and structured thinking to solve non-trivial operational challenges. You’ll work on applying AI models and data science approaches to complex enterprise datasets, helping uncover insights, automate workflows, and prototype intelligent systems quickly. The ideal person is highly curious, pragmatic, and comfortable operating in ambiguity. You don’t need to be a production software engineer, but you do need to be technically capable, thoughtful, and able to independently execute meaningful work. What You’ll Do: Work with large, messy, real-world enterprise datasets Apply LLMs and AI tooling to operational and analytical problems at scale Build data workflows and experiments using Python and Jupyter notebooks Run and analyse large-scale queries and model outputs Prototype and iterate quickly on AI-driven approaches Work closely with product, engineering, and founders on exploratory projects Translate ambiguous problems into structured investigations and solutions Help shape how AI is applied across procurement and supply chain workflows You Might Be a Great Fit if You : Have 3+ years of hands-on experience in data science, applied AI, analytics, or similar work OR a strong academic background (e.g. Master’s in Data Science, Machine Learning, Statistics, Mathematics, Computer Science, Physics, etc.) combined with 1–2 years of industry experience Strong Python skills Experience working in Jupyter notebooks Familiarity with LLMs, AI tooling, or applied machine learning workflows Strong analytical and problem-solving ability Comfort working independently on open-ended problems Ability to work pragmatically rather than over-engineering solutions Curiosity and enthusiasm for AI-native ways of working
