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. Further information is available at www.paysafe.com.
It starts here. Have a global impact on the world of payments.
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.
What Paysafe stands for
- Being open and honest.
- Keeping focused.
- Operating with Courage.
- Pioneering the future.
Our values and culture are driven by Equality, Development, Social Responsibility and Wellbeing. If you want to find out more about life at Paysafe, check out our careers page here.
How we work
We follow a hybrid working model, spending an average of three days per week at our office location. This will be based in our Dublin hub. The Dublin office is located in George's Quay in the heart of Dublin.
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.
- Proven track record of developing and deploying ML models in production environments.
- Experience working with cloud platforms (AWS, Azure, etc.) and developing ML models in cloud environments.
- Strong communication skills, with the ability to explain complex analytical concepts to non‑technical stakeholders.
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
- 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.
- We also offer: Private health insurance (pre‑existing conditions are included) & 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.
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