Senior Manager, Data Science

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

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.

Qualifications:

• 7-9+ years of experience in Data Science, Machine Learning or Advanced Analytics within a commercial environment, with a strong track record of delivering production-grade analytical solutions.
• 3-5+ years of people management experience, leading, coaching and developing Data Science, Analytics or Machine Learning professionals, with demonstrated success in building high-performing teams.
• Strong experience in payments, fraud prevention or financial services, with a solid understanding of transaction risk, fraud detection, risk decisioning and the application of machine learning to real-world payment challenges.
• Strong technical proficiency in Python, SQL and modern Data Science tools, with hands-on expertise in machine learning, predictive modelling, statistical analysis and experimentation.
• Proven experience developing, deploying and monitoring scalable machine learning and AI solutions, including providing technical leadership across multiple projects and initiatives.
• Strong leadership and stakeholder management capabilities, with the ability to translate complex analytical concepts into actionable business recommendations and influence senior stakeholders.
• Demonstrated ability to balance technical excellence, model performance, governance and operational requirements while delivering measurable business impact.
• Familiarity with model governance, responsible AI, data privacy and regulatory considerations within financial services or payments environments.

Education Qualifications:

• Master’s degree in Data Science, Artificial Intelligence, Statistics, Mathematics, Computer Science, Economics or a related quantitative field preferred.
• A Bachelor’s degree in a relevant discipline combined with substantial equivalent professional experience will also be considered.
• A strong academic or professional foundation in machine learning, statistical modelling and data-driven problem solving is highly desirable.

A snippet of what you’ll get in return:

• Make your day work for you with our flexible working hours.
• You decide what your holiday looks like with the option to buy or sell your holiday and carry over up to 5 days into the next year.
• Enjoy social events on our roof top terrace with views onto St Pauls Cathedral.
• Our fully equipped facilities include showers, hairdryers and straighteners and fresh towels.
• Start your day with a free breakfast, fresh fruit and snacks.
• Take a breather in our dedicated wellbeing room.
• Spend time with those important to you with our enhanced paid family policies.
• Test our products Skrill and Neteller. Upon joining we will award you £50 into each wallet.
• Enjoy our discounts on memberships via vitality including, gyms, leisure centres, yoga/Pilates across the country.
• Need a new Laptop or TV? We offer support purchasing Apple and LG products via Stormfront technology.
• Join our six employee-led equality communities and help foster a workplace that celebrates diversity and create opportunities to collaborate and learn.
• Give back to the community with four paid charity days.
• Kickstart your weekend early with our summer hours during the months of June, July and August with a 3pm finish every Friday.
• Let’s not forget, we also offer: Private health insurance (pre-existing conditions are included) & dental insurance, income protection, life assurance and more.

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.