Senior Data Scientist

Responsibilities

• Design, build, and deploy machine learning models that solve real-world financial crime and risk challenges.
• Work on problems such as fraud detection, chargeback prediction, anomaly detection, identity verification, and transaction monitoring.
• Transform ambiguous business problems into measurable ML solutions.
• Partner closely with Product and Operations teams to define requirements, success metrics, and decision frameworks.
• Analyse large datasets to identify patterns, opportunities, and risks.
• Build and maintain production-ready data pipelines and model-serving solutions.
• Monitor model performance and continuously improve accuracy, reliability, and business impact.
• Collaborate with Data Engineering and Backend Engineering teams to operationalise machine learning at scale.
• Help shape the future of AI and machine learning capabilities across ARQ.

Requirements

• This is a highly impactful role where you'll take problems from idea to production - partnering with Product, Engineering, and Operations teams to turn large-scale datasets into models that directly influence customer and business outcomes.
• You'll join a fast-growing team helping build the future of financial services for more than 2 million customers across the Americas.
• 5+ years of experience across Data Science, Machine Learning, Software Engineering, or related disciplines.
• Strong Python skills and experience working with large-scale datasets.
• Proven experience developing and deploying machine learning models in production environments.
• Strong understanding of supervised learning techniques, particularly classification problems.
• Experience with anomaly detection, fraud prevention, risk modeling, or related domains.
• Ability to translate business challenges into data-driven solutions.
• Experience working cross-functionally with Product, Engineering, and business stakeholders.
• Strong communication skills and a pragmatic approach to problem-solving.
• Comfortable operating in fast-moving environments with high ownership.
• Experience in financial crime, fraud prevention, AML, risk, or payments.
• Experience in fintech, banking, or financial services.
• Backend engineering experience and familiarity with production systems.
• Experience building real-time decisioning or risk platforms.
• Familiarity with modern MLOps practices and model monitoring.

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