Senior Data Scientist

Paysafe is a global payments platform powering the experience economy, with a strong focus on the iGaming, video gaming, e-commerce, retail, travel and hospitality sectors. With 30 years of expertise in payment technology, Paysafe helps businesses and consumers lift every experience through seamless, secure payment solutions, including card payments, digital wallets such as Skrill, eCash solutions like PaysafeCard, and a suite of local payment methods. With approximately 2,900 employees across 12 countries and an annualized transactional volume of $167 billion in 2025, Paysafe connects people and businesses worldwide through innovative digital payment experiences.

We are looking for a Senior Data Scientist to join our Consumer Risk Data Science team, focused on machine learning modelling for fraud detection and financial crime prevention within the payments ecosystem. This is a hands‑on, model development‑focused role, where you will design, build, and deploy machine learning models to detect and prevent fraud and financial crime across the customer lifecycle. You will work with large‑scale, high‑dimensional transactional data in a fast‑paced environment, contributing to high‑impact solutions that protect the business and its customers.

Key responsibilities

• Develop and deliver end‑to‑end data science solutions, from problem definition through model development to production deployment.
• Build and optimise machine learning models, selecting appropriate techniques based on data characteristics and problem context.
• Perform advanced feature engineering on large‑scale, high‑dimensional transactional datasets to improve model performance.
• Apply robust validation frameworks (e.g., time‑based splits, OOT testing) to ensure model stability and generalisation.
• Productionise models following best practice MLOps standards, working closely with Engineering teams.
• Monitor model performance and iterate based on data drift and evolving fraud patterns.
• Collaborate with cross‑functional teams to ensure scalable integration of models into production systems.
• Communicate analytical approaches and results clearly to both technical and non‑technical stakeholders.
• Contribute to team best practices, documentation, and continuous improvement of modelling standards.
• Mentor junior team members and, where applicable, support the development of data scientists.

Required skills and experience

• 5–7+ years of experience in data science / machine learning roles.
• Strong proficiency in Python (NumPy, Pandas, Scikit‑learn, etc.).
• Solid understanding and hands‑on experience building machine learning models (supervised and unsupervised).
• Strong knowledge of:

• Data preprocessing and feature engineering.
• Model validation techniques (including time‑based / OOT approaches).
• Model evaluation and performance metrics.

Preferred

• Experience in fraud, risk, or payments domain.
• Experience with real‑time or near real‑time modelling environments.
• Interest or exposure to advanced techniques (e.g., graph modelling, network analytics, sequence modelling).

Education

• Bachelor’s or master’s degree in a quantitative field (e.g., Computer Science, Mathematics, Statistics, Engineering). Advanced degree is a plus.

A snippet of what you’ll get in return

• Flexible working hours.
• Option to buy or sell your holiday and carry over up to 5 days into the next year.
• Social events on our roof top terrace.
• Fully equipped facilities including showers, hairdryers and straighteners.
• Free breakfast, fresh fruit and snacks.
• Dedicated wellbeing room.
• Enhanced paid family policies.
• £50 into each wallet upon joining.
• Discounts on memberships via vitality including gyms, leisure centres, yoga/Pilates.
• Support purchasing Apple and LG products via Stormfront technology.
• Join our six employee‑led equality communities.
• Four paid charity days.
• Summer hours during June, July and August with a 3pm finish every Friday.
• Private health insurance & dental insurance, income protection, life assurance and more.

What to expect next

• Phone screen with Talent Acquisition.
• Video interview with the Hiring Manager.
• Technical interview with one of the Data Scientists.
• In‑person interview with Chief Data & AI Officer and final HR interview with Talent Acquisition.

If you’re successful joining the team, you’ll be meeting our CEO in person during our new joiners breakfast in London – a great opportunity to network with your peers.

Equal Employment Opportunity

Paysafe is an equal opportunity employer. We value diversity and are committed to providing a work environment of mutual respect to everyone without regard to race, color, religion, national origin, age, gender identity or expression, or any other characteristic protected by applicable laws, regulations and ordinances.