Data Scientist

Paystone

Remote Data Scientist at Paystone building machine learning models that impact customer experiences. Collaborating with cross-functional teams to turn data into actionable insights and systems.

Posted 6/10/2026full-timeRemote • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Partner with Product and BI to turn business goals into well-scoped ML problems with clear success criteria and guardrails.
Build and productionize models for segmentation, churn, LTV, and recommendations.
Design evaluation frameworks including offline benchmarks and online feedback loops to ensure models are measured, not assumed.
Collaborate with Engineering on production design considerations like latency, payload structure, fallbacks, and failure modes.
Monitor model performance over time, detect drift, and own retraining and iteration cycles.
Use product telemetry and user feedback to drive continuous model and feature improvements.
Work alongside AI coding agents to accelerate development (pipelines, tests, refactoring, exploration) while maintaining human oversight.
Communicate results, trade-offs, and recommendations clearly across technical and non-technical teams.

Requirements

What you’ll need

5+ years as a Data Scientist, Applied Scientist, or ML Engineer.
Strong SQL and Python skills with production ML experience.
Experience with churn, segmentation, LTV, or recommender systems.
Comfortable working with production systems and APIs.
Familiar with AI coding tools and agentic workflows.
Strong communicator who can simplify technical concepts.
Nice to Have: Experience in loyalty, CRM, or retail analytics; LLM or AI agent experience; Building internal tools or ML infrastructure; French/English bilingual.

Benefits

Comp & perks

Flexibility: People-first approach focused on outcomes, not location or hours.
Collaboration: Partner closely with Product, Engineering, and BI in a fast-moving, supportive environment.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

SQLPythonmachine learningmodel productionizationsegmentationchurn analysisLTV modelingrecommendation systemsmodel evaluation frameworksmodel performance monitoring

Soft Skills

strong communicationsimplifying technical conceptscollaborationproblem-solvingiteration cycles management