Applied Scientist, Prolific

• The ideal candidate will help define the right problems to solve, not just execute against a fixed brief
• 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

Desirable

• 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 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