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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 ****, Paysafe connects people and businesses worldwide through innovative digital payment experiences.
Further information is available at .
It starts here.
Have a global impact on the world of payments.
We are looking for a Lead Data Scientist to join our Consumer Risk Data Science team, focused on machine learning modelling in fraud detection and financial crime prevention within the payment ecosystem.
This role combines deep hands-on modelling expertise with technical leadership and domain ownership.
You will lead the design and development of advanced machine learning solutions, set modelling standards, and drive best practices across the team.
You will play a key role in shaping the evolution of our modelling capabilities, working with large-scale transactional data and collaborating closely with Engineering, Product, and Risk teams to deliver scalable, high-impact solutions.
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
Lead the development and delivery of
end-to-end data science solutions
, from problem definition through model development to production deployment
Own and drive modelling approaches
for key fraud / financial crime use cases across the customer lifecycle
Build and optimize
machine learning models
, selecting and guiding the use of appropriate techniques based on data and problem context
Define and enforce
best practices in feature engineering, model validation, and experimentation frameworks
Oversee
model productionisation
, ensuring alignment with MLOps standards and scalable deployment patterns
Drive
model performance monitoring, governance, and continuous improvement
across the portfolio
Collaborate with cross-functional teams to ensure
effective integration of models into production systems and workflows
Act as a
subject matter expert (SME)
in fraud modelling and advanced analytics, providing guidance to stakeholders and team members
Mentor and develop data scientists
, and where applicable, provide line management and support team growth
Contribute to
technical strategy, tooling, and long-term evolution
of data science capabilities within the team
Required skills and experience
7–10+ years of experience in data science / machine learning roles
Strong proficiency in
Python
(NumPy, Pandas, Scikit-learn, etc.)
Proven track record of
developing and deploying ML models in production environments
Strong hands-on experience with:
Feature engineering at scale
Model validation frameworks (including time-based / OOT approaches)
Supervised and unsupervised learning techniques
Experience working with
cloud technology stacks
(AWS, Azure, etc.)
Demonstrated ability to
lead complex data science projects and influence technical direction
Strong communication skills, with the ability to explain complex analytical concepts to non-technical stakeholders
Strongly preferred
Experience in
fraud, risk, or payments
domain
Experience with
real-time or near real-time modelling environments
Exposure to
graph-based modelling, deep learning, or advanced ML techniques
(Sequential modeling, representation learning, transformer etc.)
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.
Let's not forget, 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.
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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