At a Glance
- Tasks: Dive into messy datasets and apply AI to solve real-world problems.
- Company: Join an innovative AI startup backed by Sequoia Capital.
- Benefits: Competitive salary, equity, flexible work, and generous holiday.
- Other info: Collaborative culture with opportunities for personal and professional growth.
- Why this job: Make a tangible impact on global supply chains with cutting-edge AI.
- Qualifications: Experience in data science or strong academic background in relevant fields.
The predicted salary is between 60000 - 70000 £ per year.
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
Bonus Points:
- Experience applying LLMs to real-world datasets
- Experience with vector databases, embeddings, or retrieval systems
- Exposure to operational or enterprise data environments
- Background in highly analytical disciplines such as medicine, physics, maths, or engineering
Compensation And Benefits
At Magentic, we recognise and reward the talent that drives our success. We offer:
- Competitive Equity: play a real part in Magentic's upside
- A salary of £60-70k
- 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 for team lunches, coffees, dinners, or activities with colleagues
- Salary sacrifice pension and nursery schemes
- Hybrid London HQ (WFH Thurs and every other Tues, with flex if you have appointments etc)
- Annual team retreat—a fully-funded off-site to recharge, bond, and build
Our Interview Process
There are a few components because it's really important that both we and you have all the information to make a great decision at this stage of our journey. We can move quickly through these stages, so let us know if you have any timelines we need to meet.
- Initial call (30 mins): this first step is an opportunity for you to hear more about Magentic and the role, and for us to learn more about how your experience aligns with the role.
- Skills interview (60 mins with some prep): in this step, we'll ask you to present some of your work to us and discuss it.
- In-person interview: for the final step, we invite you to come meet the team in-person and work alongside us! We find this is the best way for candidates to get a sense of what working at Magentic is like. This day will include a culture interview, a role-specific task and a discussion of your work with the team.
Responsible AI Statement
At Magentic, we are committed to developing artificial intelligence that benefits humanity. We push the limits of AI's capabilities and are dedicated to its responsible and safe deployment. Recognising the profound impact of AI, we ensure that its development is centred around human needs and safety, incorporating a wide array of perspectives to fulfil our mission.
Equal Opportunities and Accommodations Statement
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.
Data Scientist employer: Magentic
At Magentic, we pride ourselves on fostering a dynamic and inclusive work culture that encourages innovation and collaboration. As a Data Scientist, you'll have the opportunity to work with cutting-edge AI technologies in a supportive environment that values your contributions and offers competitive equity, generous benefits, and ample opportunities for professional growth. Our hybrid London HQ allows for flexibility while also promoting team bonding through regular social events and an annual retreat, making it an exciting place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Get to know the company before your interview! Research Magentic's projects and values, especially their focus on AI in supply chains. This will help you tailor your answers and show that you're genuinely interested in what they do.
✨Tip Number 2
Prepare to showcase your skills! Bring examples of your work with Python and LLMs to the interview. Being able to discuss your hands-on experience with real-world datasets will set you apart from other candidates.
✨Tip Number 3
Practice problem-solving on the spot! Since the role involves tackling ambiguous challenges, be ready to think aloud during the interview. Show how you approach complex problems and how you can break them down into manageable parts.
✨Tip Number 4
Don’t forget to ask questions! Use the interview as a chance to learn more about the team dynamics and the projects you'll be working on. This not only shows your interest but also helps you gauge if it's the right fit for you.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Show Your Curiosity:When writing your application, let your curiosity shine through! Share examples of how you've tackled complex problems or explored new data science techniques. We love seeing candidates who are eager to learn and experiment.
Be Clear and Structured:We appreciate a well-structured application that clearly outlines your experience and skills. Use bullet points or headings to make it easy for us to see how you fit the role. Remember, clarity is key!
Tailor Your Application:Make sure to tailor your application specifically for the Data Scientist role at Magentic. Highlight relevant experiences with LLMs, Python, and any hands-on projects that relate to our work in AI and supply chain solutions.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it helps us keep everything organised on our end.
How to prepare for a job interview at Magentic
✨Know Your Data Inside Out
Before the interview, dive deep into your past projects involving messy datasets. Be ready to discuss specific challenges you faced and how you applied Python and AI tooling to solve them. This will show your hands-on experience and problem-solving skills.
✨Showcase Your Curiosity
Magentic values curiosity, so come prepared with questions about their AI applications in procurement and supply chain workflows. This not only demonstrates your interest but also helps you understand how you can contribute to their mission.
✨Prepare for Practical Tasks
Since the interview includes a role-specific task, practice working with Jupyter notebooks and running large-scale queries. Familiarise yourself with LLMs and think about how you would prototype solutions quickly. This will help you feel more confident during the practical part of the interview.
✨Embrace Ambiguity
Magentic is looking for someone comfortable operating in ambiguity. Think of examples from your experience where you tackled open-ended problems. Be ready to explain your structured approach to finding solutions, as this will resonate well with their expectations.
