Senior Applied Scientist, Marketplace Optimization

Greenhouse Software, Inc.; Greater London; Remote; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Remote-friendly

Job summary

Prolific is seeking a senior Applied Scientist to join our Supply Data Science team. You’ll drive marketplace dynamics—boosting participant supply, balancing demand, and improving quality through optimization, recommendation systems, and production-ready ML.

You’ll collaborate with product and engineering, translate loosely defined problems into clear hypotheses and experiments, and steer technical directions with a pragmatic, evidence-based mindset.

Qualifications

• Deep expertise in mathematical optimization and recommendation algorithms applied to large-scale matching.
• Judgement about when to reach for simple statistics, classical ML, LLMs, or agentic approaches.
• 5+ years applying ML, AI research, optimization, or recommendation systems to complex production 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.

Responsibilities

• Design, validate, and scale production-ready algorithmic systems for marketplace matching.
• Collaborate with product and engineering teams to align solutions with business goals.
• Identify high-impact problems, run experiments, and measure outcomes.

Skills

Optimization

Recommendation

ML

Python

Experiment design

Ambiguity handling

Education

MSc/PhD preferred

Advanced degree

Job description

Prolific is seeking a senior Applied Scientist to join our Supply Data Science team. You’ll drive marketplace dynamics—boosting participant supply, balancing demand, and improving quality through optimization, recommendation systems, and production-ready ML.

You’ll collaborate with product and engineering, translate loosely defined problems into clear hypotheses and experiments, and steer technical directions with a pragmatic, evidence-based mindset.