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About the Role

We are seeking a Data Scientist to 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. This role involves working deeply with data, experimentation, AI tooling, and problem solving, using Python, notebooks, and LLMs to tackle non-trivial operational challenges.

Responsibilities

• 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

Requirements

• 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
• Experience applying LLMs to real-world datasets (Bonus)
• Experience with vector databases, embeddings, or retrieval systems (Bonus)
• Exposure to operational or enterprise data environments (Bonus)
• Background in highly analytical disciplines such as medicine, physics, maths, or engineering (Bonus)

Skills

• Python
• Jupyter notebooks
• LLMs
• AI tooling
• Applied machine learning
• Data science
• Analytics
• Problem-solving
• Vector databases
• Embeddings
• Retrieval systems

Location

• London HQ
• Hybrid

Work Type

• Hybrid
• Full-time

Experience Level

• 3+ years of hands-on experience in data science, applied AI, analytics, or similar work
• 1–2 years of industry experience with a strong academic background

Education Level

• Master’s in Data Science, Machine Learning, Statistics, Mathematics, Computer Science, Physics, or similar

Salary/Compensations

• £60-70k

Benefits

• Competitive Equity
• Enhanced parental leave
• 25 days holiday exc bank holidays, plus an extra day for our Christmas shutdown
• In-office lunches provided
• Monthly organised socials and an additional flexible monthly social budget
• Salary sacrifice pension and nursery schemes
• Annual team retreat

About the Company

• Magentic is building AI systems that can autonomously run complex procurement and supply chain workflows for large companies.
• We are tackling a hard real-world problem, helping global manufacturing supply chains become more resilient.
• We are an early-stage company backed by Sequoia Capital, with a team bringing experience from OpenAI, Meta, Revolut, NASA and McKinsey & Company.

Equal Opportunity

• Magentic is committed to creating a diverse and inclusive workplace and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, gender, gender reassignment, marital or civil partnership status, age, disability, pregnancy or maternity, or any other basis as protected by the Equality Act 2010.
• We actively encourage applications from candidates of all backgrounds and cultures and believe a diverse workforce enhances our ability to deliver innovative solutions.
• We also ensure that our employment decisions are based solely on individual qualifications, merit, and business needs.
• Magentic is dedicated to providing reasonable accommodations to job applicants with disabilities. If you require any adjustments during the recruitment process, please indicate this in your application or contact us directly.