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Salary
≈ $23k – $47k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior · 4+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 29, 2026. First seen by Alion on Aug 6, 2026. Nium scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Nium is a cross-border payments infrastructure company whose platform lets banks, payment firms, travel companies and other businesses send money to recipients in more than 100 countries, collect funds, issue virtual and physical cards and convert currencies through a single API. It began in 2014 as the remittance business Instarem and holds payment licences in markets including Singapore, the United Kingdom, the European Union and the United States. The company is headquartered in San Francisco, California, United States.

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:

  • 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

  • Understand the coverage and accuracy of the existing rules for anti-money laundering and financial crimes

  • Support rules management process to ensure rules are performing to expected thresholds

  • Support the integrity, accuracy, and usability of data across compliance and financial crime functions.

  • Develops data dashboards to provide visibility of performance of rules and models

  • Leverage advanced analytics to understand cause and effect relationships

  • Understand data quality and labelling, perform advanced analysis to identify high predictive strength variables, and work with technology teams on availability of variables.

Key Responsibilities

    • Design, deploy, and monitor predictive models and AI algorithms to detect anomalies, fraud, or potential breaches

    • Conduct deep-dive analyses into risk events, identifying root causes to improve risk strategies and operational workflows

    • Analyze large datasets to identify patterns, anomalies, and emerging risks.

    • Performs data validation, cleansing, and reconciliation for regulatory reporting.

    • Ensure alignment with regulatory requirements by building scalable reporting platforms and documenting data protocols

    • Partner with legal, product, and operations teams to translate complex technical findings into actionable business insights for senior management

    • Maintains auditability, traceability, and evidence generation within systems.

Requirements

    • Degree in Statistics, Mathematics, Data Science, Economics, or related quantitative field

    • 4-6 years in data science, advanced analytics, or machine-learning roles.

    • Proficiency in various model development techniques

    • Prior experience in financial services, fintech, payments, or consulting would be strongly preferred

    • Exposure to financial-crime systems (e.g., transaction monitoring, sanctions screening, case-management platforms).

    • Experience supporting compliance operations, investigations, or model governance.

    • Experience building and deploying ML models in production environments.

    • Strong analytical rigor, proactive problem-solving, and capability to communicate technical concepts to non-technical partners

    • Self-motivated, adept in working individually and as part of a global team

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