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Sr Data Scientist
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What you'll do
- Significantly enhance Nium’s ability to detect emerging risks, optimise controls, meet regulatory expectations, and drive operational efficiency across the compliance function
- Support strategic priorities including automation, risk‑based decisioning, model optimisation, data‑quality improvement, and regulatory assurance
- Support activities related to transitioning compliance systems to advanced, data-driven Artificial Intelligence / Machine Learning solutions e.g. Transaction Monitoring detection models
- Development of AI/ML models to identify risks (such as fraud or credit risk) while ensuring these models are auditable, explainable, and compliant with data privacy laws
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Nium provides global infrastructure for real-time cross-border payments. We were founded on the mission to deliver the global payments infrastructure of tomorrow, today. Our platform enables banks, fintechs, and global businesses to move money instantly, everywhere.
Co-headquartered in San Francisco and Singapore with offices in 14 markets worldwide, we are entering one of the most exciting chapters in our journey. In March 2026, we delivered the largest month in our 11-year history with record revenue, record volumes, and EBITDA profitability. Today, Nium moves nearly $60B in payments annually, almost entirely for enterprises, while continuing to strengthen an already healthy balance sheet.
It is an incredible time to join us, and we are only just getting started.
Our payout network spans 190+ countries and 100 currencies, with 100 + corridors in real time. We power seamless transfers to accounts, wallets, and cards, support local collections in 35 markets, and as a principal card issuer on Visa, Mastercard, Discover, and UATP, Nium issues over 50 million card tokens every year. Backed by regulatory licenses in 40+ markets, we make it simple for our partners to onboard, integrate, and scale globally. This scale and innovation have earned us recognition as one of CNBC’s World’s Top Fintech Companies 2025, winner of Best Cross-Border Payments Solution at the PayTech Awards, and inclusion in FXC Intelligence’s Top 100 Cross-Border Payments Companies list.
In 2024, we raised US$50 million in Series E funding at a US$1.4 billion valuation to accelerate network expansion, product innovation, and talent growth. With the B2B payments market projected to hit US$175 trillion by 2030, Nium offers ambitious builders the chance to shape the future of global money movement with the scale of a leader and the energy of a high-growth company.
The increasing volume, complexity, and regulatory scrutiny of financial‑crime and compliance risks requires advanced, data‑driven capabilities that traditional analytics and manual processes can no longer support. The Compliance Data Scientist will significantly enhance Nium’s ability to detect emerging risks, optimise controls, meet regulatory expectations, and drive operational efficiency across the compliance function.
This role fills a critical capability gap and directly supports strategic priorities including automation, risk‑based decisioning, model optimisation, data‑quality improvement, and regulatory assurance. Additionally, this role is specifically designed to support activities related to transitioning compliance systems to advanced, data-driven Artificial Intelligence / Machine Learning solutions e.g. Transaction Monitoring detection models.
Role Summary:
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Responsible for development of AI/ML models to identify risks (such as fraud or credit risk) while ensuring these models are auditable, explainable, and compliant with data privacy laws
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Understand the coverage and accuracy of the existing rules for anti-money laundering and financial crimes
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Support rules management process to ensure rules are performing to expected thresholds
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Support the integrity, accuracy, and usability of data across compliance and financial crime functions.
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Develops data dashboards to provide visibility of performance of rules and models
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Leverage advanced analytics to understand cause and effect relationships
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Understand data quality and labelling, perform advanced analysis to identify high predictive strength variables, and work with technology teams on availability of variables.
Company
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