At a Glance
- Tasks: Shape the future of AI by validating high-quality expert participants and solving real-world problems.
- Company: Join Prolific, a leader in human data infrastructure for next-gen AI systems.
- Benefits: Enjoy competitive salary, remote work, and a mission-driven culture.
- Other info: Dynamic role with opportunities to shape your career and collaborate with diverse teams.
- Why this job: Be at the forefront of AI innovation and make a real impact on technology.
- Qualifications: 3+ years in ML, statistics, or data science with strong Python skills.
The predicted salary is between 50000 - 70000 £ per year.
Prolific is building the human data infrastructure that powers the next generation of AI systems. As frontier AI labs scale their use of human-generated data for training, evaluation, and alignment, the way we measure quality, performance, and operational efficiency becomes increasingly important.
You'll join Prolific's Integrity team, which protects the platform from fraud and misuse. This is a new role focused on verification - in particular identifying and validating high quality expert participants. This is an emerging area, so part of the role will be to shape the problem space: working with other teams to find, validate and model useful signals for participant quality. The ideal candidate will help define the right problems to solve, not just execute against a fixed brief.
The Applied Scientist role combines data science, ML and statistics to build practical systems that drive real business outcomes. This isn't a pure research role, although you may explore papers and explore new ideas. The focus is on turning promising approaches into production ready solutions. It is also not an ML engineering role: you don't need engineering experience, but you should understand what deploying a model involves and the practical constraints around it. We care about approaches that actually work in production, not ones that are too slow or expensive to run, and about the judgement to tell the difference.
You'll be the first data person in this area, so you'll help shape the role and create structure where there isn't much yet, with support from leadership. You don't need to have all the answers on day one. You spot problems worth solving and start on them, rather than waiting to be assigned. You will be comfortable (and ideally enjoy) collaborating with others.
What We are looking for:
- Deep expertise in one applied ML, statistics, or data science specialism. Examples could be risk modelling, LLM detection, and reinforcement learning, but any area of genuine depth counts.
- Judgement about when to reach for simple statistics, classical ML, LLMs, or agentic approaches.
- 3+ years applying ML, AI research, or data science to real problems.
- Python skills sufficient to build, test, and iterate on working prototypes independently.
- Able to take a loosely defined product or customer problem and turn it into clear hypotheses, experiments, and metrics.
- You must be comfortable working on ambiguous problems, and suggesting solutions rather than waiting to be told exactly what to do.
Nice to have:
- An MSc or PhD in Computer Science, Maths, Statistics, ML, or a related field — or equivalent knowledge gained another way.
- Experience working alongside product and engineering teams.
Why Prolific is a great place to work:
We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems. We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity. Working for us will place you at the forefront of AI innovation, providing access to our unique human data platform and opportunities for groundbreaking research. Join us to enjoy a competitive salary, benefits, and remote working within our impactful, mission-driven culture.
StudySmarter Expert Advice🤫
We think this is how you could land Applied Scientist (Integrity) 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 Prolific!
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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 Prolific.
✨Apply Directly through Our Website
When you find a suitable opening like Applied Scientist (Integrity) at Prolific, 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 Applied Scientist (Integrity) 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 Prolific, 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 Prolific. 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 Prolific
✨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 Prolific!
✨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.