Lead Data Scientist - Spend

Location: Global

Team: General

About the Role

Wise is a global technology company building the next generation of financial services, with a mission to make international money movement seamless, fast, and affordable. As a Lead Data Scientist on the Spend team, you will leverage your expertise to innovate and deploy advanced machine learning models that enhance card fraud detection and optimize card product performance. Your work will directly safeguard customers during transactions while driving adoption and retention across global markets. You will lead the analysis of massive transaction datasets to uncover fraud patterns and customer behaviors, design experiments for continuous improvement, and deploy LLM-based automation for risk handling. Collaborating with engineering, product, risk, and compliance teams, you'll build real-time data pipelines and mentor junior scientists to foster a culture of excellence. This role demands a strong technical foundation in Python, ML frameworks, and anomaly detection, paired with a product mindset and passion for protecting users. Join a diverse, inclusive team at Wise, where flexible working, stock options, health benefits, and mission-driven impact await. Help us create money without borders for everyone, everywhere.

Key Responsibilities

• Lead the development and deployment of advanced machine learning models to enhance card fraud detection 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

Required Qualifications

• Proven experience in a data science role
• 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

Preferred Qualifications

• Experience related to card domain, fraud detection, anti-money laundering, or fintech related domains
• Experience with compliance in assuring effectiveness of controls
• Experience collaborating with engineering on services

Required Skills

• Python (solid knowledge, able to make and justify design decisions)
• Git (e.g., opening Pull Requests on GitHub, code review)
• Ability to read through code, especially Java
• Range of model types (gradient boosting, neural networks, regression, autoencoders, clustering)
• Statistical analysis
• Good presentation skills
• Strong product mindset
• Good communication skills for non-technical individuals
• Strong problem solving skills
• Experience with real-time data analysis

Benefits

• RSUs (stock options)
• Flexible working
• Parental leave
• Learning budget
• Paid sabbatical after 4 years
• Health insurance
• Company retreat
• Wise card

Wise is an equal opportunity employer committed to building a diverse workforce.