Senior Data Scientist - Spend

Company Description

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 Description

We’re looking for a Senior Data Scientist to join our growing Spend Team in Tallinn. 

This role is a unique opportunity to work behind the scenes of the card product lifecycle, understand how we prevent and manage risks while delivering the seamless spending experience our customers deserve. What you build will have a direct impact on  Wise’s mission and millions of our customers.

Qualifications

Role Overview:

As a Senior 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.

Key Responsibilities:

• Analyze large volumes of transaction data to identify trends, patterns, and anomalies associated with fraudulent card activity and customer behavior.
• Drive 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.
• Design and implement experiments to evaluate the effectiveness of fraud detection systems and card product features, continuously improving their performance.
• Design and implement machine learning solutions for customer churn prediction to proactively identify at-risk card customers and inform targeted retention strategies across Wise markets.
• 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.

A bit about you: 

• Proven experience in a data science role, bonus if experience is related to card domain, fraud detection, anti-money laundering, or fintech related domains;
• Solid knowledge of Python, and ability 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.
• Experience 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.
• Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL).
• Familiarity designing and deploying LLM-based solutions in production.
• 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.
• 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.

Additional Information

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.

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