About Paysafe

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 annualized transactional volume of $167 billion in 2025, Paysafe connects people and businesses worldwide through innovative digital payment experiences. Further information is available at www.paysafe.com.

It starts here. Have a global impact on the world of payments.

About the Role

As a Senior Manager, Data Science at Paysafe, you will lead and develop a team of Data Scientists while remaining actively involved in the design, development and delivery of advanced analytics, machine learning and AI solutions. Working in a fast-paced global payments environment, you will apply data science to complex business challenges, particularly in payments and fraud prevention, using large-scale transactional, customer, risk and operational data to deliver measurable commercial and operational outcomes.

This is a hands-on people leadership role, combining formal team management with direct technical contribution and model delivery. You will be expected to personally develop and deliver machine learning models alongside your team, while providing technical direction, reviewing modelling approaches and ensuring the quality of solutions delivered. You will take ownership of complex or high-priority modelling initiatives, from problem definition and feature engineering through model development, validation and production deployment.

You will partner with Product, Engineering, Commercial, Risk, Finance and Operations to translate business needs into scalable data science solutions. This role reports to our Senior Director of Data Science and forms part of a wider Data Science organisation of over 25 data scientists.

What Paysafe Stands For

• Being open and honest.
• Keeping focused.
• Operating with courage.
• Pioneering the future.

How We Work

We follow a hybrid working model, spending an average of three days per week at our office location. The office is located in Gresham Street next to St Paul’s cathedral with easy access and transport links via St Paul’s, Bank, Cannon Street, City Thameslink, Liverpool Street, Farringdon, Mansion House.

Responsibilities

• Hands-on Data Science Delivery: Lead and contribute to the design, development, deployment and monitoring of machine learning models, predictive analytics and advanced statistical solutions. Remain hands-on to develop and deploy models, review code, challenge modelling choices, resolve complex technical issues and contribute directly to priority initiatives.
• Team Leadership and Development: Manage, mentor and develop a high-performing team of Data Scientists. Set clear objectives, provide regular feedback, support career development and create an inclusive environment that encourages collaboration, innovation, accountability and continuous learning.
• Technical Direction and Quality: Provide technical oversight across the team’s portfolio, ensuring appropriate methodologies, robust validation and high standards of analytical quality. Promote consistent practices for experimentation, documentation, reproducibility, model monitoring and maintainability.
• Business-Focused Problem Solving: Translate complex business challenges into practical data science opportunities and ensure solutions deliver measurable value. Balance analytical sophistication with scalability, operational practicality, risk considerations and commercial priorities.
• Cross-functional Collaboration: Work closely with Product, Engineering, Commercial, Risk, Finance, Compliance and Operations teams to deliver scalable solutions. Build trusted stakeholder relationships and communicate complex analytical findings clearly to technical and non-technical audiences.
• Planning and Delivery Management: Plan and oversee multiple Data Science initiatives, prioritise team capacity, manage dependencies and delivery risks, and ensure work is completed to agreed standards and objectives. Escalate material risks and strategic decisions to the relevant Director.
• Governance and Responsible AI: Ensure Data Science solutions follow internal governance, privacy, security and responsible AI requirements, as well as applicable regulatory and industry standards. Support appropriate model documentation, approval, monitoring and controls.
• Innovation and Continuous Improvement: Identify opportunities to improve Data Science processes, tools and ways of working. Stay current with developments in machine learning, AI and advanced analytics, and assess where emerging approaches could deliver relevant business value.
• Capability Building: Support recruitment, onboarding and workforce planning for the team. Encourage knowledge sharing, reusable assets and stronger collaboration across Data Science, Data Engineering, Analytics and other technical communities.