Job description

WHAT WE'RE LOOKING FOR We're looking for a Senior Data Scientist to help build the intelligence layer behind ARQ's financial products. You'll work on some of our most important risk and financial crime challenges, developing machine learning models that help us detect fraud, prevent losses, and make better decisions at scale. 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. WHAT YOU'LL BE DOING - 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. WHAT YOU'LL NEED - 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. NICE TO HAVE - 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. BENEFITS - Competitive salary and benefits - Stock options, so you own part of what you build - Discretionary performance bonus - The latest tools and technology - A world-class team that will challenge and grow your skills - The opportunity to help build the best fintech app in Latin America - Office Policy: 3-4 days a week in-office

Requirements

Must-haves

You'll join a fast-growing team helping build the future of financial services for more than 2 million customers across the Americas.
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.
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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaipython

Platform: ashby
Effort: quick