Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.
Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.
As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.
More about our mission and what we offer.
Job DescriptionAs a Lead Data Scientist on the Spend team, you will leverage your expertise in data science to innovate and deploy models that enhance our card fraud detection capabilities and optimize card product performance. Your work will directly influence our ability to safeguard customers during card transactions while driving card adoption and retention. You will collaborate closely with cross-functional teams, including engineering, product, and risk management.
• Lead the development and deployment of advanced machine learning models to enhance our detection of card fraud and optimize card product performance across different Wise markets.
• Analyze large volumes of transaction data to identify trends, patterns, and anomalies associated with fraudulent card activity and customer behavior.
• Design and implement experiments to evaluate the effectiveness of fraud detection systems and card product features, continuously improving their performance.
• Design and deploy LLM-based risk handling automation components to enhance decision-making processes and streamline risk response workflows.
• Collaborate with analysts, risk teams and engineers to translate business requirements into actionable data insights and solutions for card issuance, fraud prevention, and retention.
• Develop robust data pipelines, algorithms, and tools to support real-time fraud detection and card product optimization.
• Stay informed about the latest advancements in data science, machine learning, and payment fraud prevention techniques to ensure state-of-the-art capabilities in the Spend domain.
• Mentor and guide junior data scientists, fostering a culture of collaboration and continuous learning within the team.
QualificationsProven experience in a data science role, bonus if experience is related to card domain, fraud detection, anti-money laundering, or fintech related domains;
Strong proficiency in machine learning frameworks and programming languages such as Python, R, or similar.
Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL).
Experience designing and deploying LLM-based solutions in production.
Familiarity with anomaly detection, supervised and unsupervised learning methods, and real-time data analysis.
Demonstrated ability to work collaboratively in cross-functional teams and effectively communicate complex technical concepts to non-technical stakeholders.
A proactive, problem-solving mindset with a passion for protecting users from criminal activities.
You have a solid knowledge of Python, and are able to make and justify design decisions in your code. You know how to use Git to collaborate with others (e.g. opening Pull Requests on GitHub) and are able to review code. Ability to read through code, especially Java. Demonstrable experience collaborating with engineering on services;
You have experience working with compliance in assuring effectiveness of controls;
You are familiar with a range of model types, and know when and why to use gradient boosting, neural networks, regression, autoencoders, clustering or a blend of these;
Experience with statistical analysis and good presentation skills to drive insight into action;
A strong product mindset with the ability to work independently in a cross-functional and cross-team environment;
Good communication skills and ability to get the point across to non-technical individuals;
Strong problem solving skills with the ability to help refine problem statements and figure out how to solve them.
For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.
We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.
If you want to find out more about what it's like to work at Wise visit Wise.Jobs.
Keep up to date with life at Wise by following us on LinkedIn and Instagram.
Top Skills
What the Team is Saying
What We Do
Wise is a global technology company, building the best way to move and manage the world's money.
With Wise Account and Wise Business, people and businesses can hold 40 currencies, move money between countries and spend money abroad. Large companies and banks use Wise technology too; an entirely new network for the world's money. Launched in 2011, Wise is one of the world’s fastest growing, profitable tech companies.
In fiscal year 2025, Wise supported around 15.6 million people and businesses, processing over $185 billion in cross-border transactions and saving customers around $2.6 billion.
Why Work With Us
We’re truly global in who we are, how we work, and how we build. Everything we do is centred around creating a world of money that’s fast, easy, fair. And open to all. Everyone who works here owns a piece of Wise, from the work they do, to the stock they hold.
Gallery
Wise Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
We expect new joiners in the office most days to build connections and learn from colleagues for their first six months. After that, most Wisers split their working week between the office and home, typically coming in at least 12 times a month.
