At a Glance
- Tasks: Lead AI research and simulations to improve public policy decision-making.
- Company: Innovative startup transforming public institutions with AI technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Join a founding team in a dynamic environment with potential for significant career advancement.
- Why this job: Make a real impact on society by shaping evidence-based policies with cutting-edge AI.
- Qualifications: 2-3 years in ML systems or research, strong Python skills, and a passion for public sector challenges.
The predicted salary is between 68205 - 80000 £ per year.
Governments and policymakers make decisions that shape millions of lives, but they still operate with slow research cycles, fragmented evidence, and expensive consultant reports that often arrive only after programs have launched. We believe AI can help public institutions make better decisions: faster, more evidence-based, more responsive, and more accountable to the people they serve. Our platform helps public institutions synthesize evidence, simulate how policy choices may affect people, budgets, and outcomes before rollout, monitor real‑world results, and continuously improve decisions over time.
A large part of our work involves extending the frontier of simulations and LLMs in the fields of policy and social sciences. You’ll be leading these efforts to benchmark various models on problems across the policy spectrum. This is a hybrid role: part applied researcher, part production engineer.
Day To Day, You’ll:
- Move fluidly between research and production, collating research, prototyping ideas and turning it into useful product.
- Benchmark simulations on new policy problems across fields.
- Build specialised research agents for social and economic policy.
- Own the AI/ML layer end‑to‑end: data pipelines, model selection, inference infrastructure, monitoring, and the feedback loops that make it better over time.
- Sit in on user research and customer calls so your work is grounded in real workflows, not assumptions.
You’re a fit if:
- 2–3 years building ML systems in production – at a startup, research lab, or product team OR 2–3 years research experience in ML or applied stats.
- Strong Python and a solid grasp of modern ML tooling, experience fine‑tuning models.
- A track record of going from research idea to working system, with a clear point of view on what’s hype and what’s real.
- Comfort with ambiguity, sparse specs, and the gap between “the model can do this” and “users can rely on this.”
- Genuine interest in public sector problems.
Nice to have:
- Exposure to government, policy, or public‑sector consulting.
- Published research, open‑source contributions, or strong writing about applied ML, especially in regulated or sociological domains.
- Experience in econometrics, causal inference.
Additional Information:
- Contract Type: Full‑Time
- Location: Paris, London, Brooklyn
- Possible partial remote
FOUNDING ENGINEER - RESEARCH in London employer: STATION F
As a pioneering force at the intersection of AI and public policy, we offer an exceptional work environment that fosters innovation and collaboration. Our hybrid role allows you to engage in meaningful research while directly impacting decision-making processes in the public sector. With a strong emphasis on employee growth, a supportive culture, and the opportunity to shape a new category in technology, joining our team means contributing to a mission that truly matters.
StudySmarter Expert Advice🤫
We think this is how you could land FOUNDING ENGINEER - RESEARCH in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like STATION F!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like FOUNDING ENGINEER - RESEARCH at STATION F.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like STATION F.
✨Apply Directly through Our Website
When you find a suitable opening like FOUNDING ENGINEER - RESEARCH at STATION F, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace FOUNDING ENGINEER - RESEARCH in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at STATION F, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at STATION F. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at STATION F
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at STATION F!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
