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Senior Data Scientist, FinCrime

Reads like a ML Engineer role: putting models into production.

London · New York · London, England, United Kingdom

Pay
Pay not listed
Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
Growth — team source
Apply at ARQ

What you’ll work on

Full posting
  • Design, build, and deploy ML models for financial crime and risk problems

  • Work on fraud detection, chargeback prediction, anomaly detection, and transaction monitoring

  • Build and maintain production data pipelines and model-serving solutions

  • Monitor model performance and keep improving accuracy and reliability

  • Turn ambiguous business problems into measurable ML solutions with Product and Operations

From the employer’s posting
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.
Build and maintain production-ready data pipelines and model-serving solutions.
Monitor model performance and continuously improve accuracy, reliability, and business impact.
Transform ambiguous business problems into measurable ML solutions.

What you’ll bring

All qualifications

Core experience

  • 5+ years of experience across Data Science, Machine Learning, Software Engineering, or related disciplines.
  • Experience in financial crime, fraud prevention, AML, risk, or payments.
  • Strong Python skills and experience working with large-scale datasets.
  • Experience in fintech, banking, or financial services.
  • Proven experience developing and deploying machine learning models in production environments.
  • Experience building real-time decisioning or risk platforms.
Qualification wording
5+ years of experience across Data Science, Machine Learning, Software Engineering, or related disciplines.
Experience in financial crime, fraud prevention, AML, risk, or payments.
Strong Python skills and experience working with large-scale datasets.
Experience in fintech, banking, or financial services.
Proven experience developing and deploying machine learning models in production environments.
Experience building real-time decisioning or risk platforms.

Tools in this posting

  • Python
Source — Tool mentions in context
- 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.

Benefits in the posting

Full benefits wording
  • 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

From the employer’s posting.

Posting record

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Status in our records
Live
First seen by us
Aug 12, 2026 · 78 days ago
Recorded sightings
113
Last seen by us
Oct 30, 2026

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Job description

View original posting ↗

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

 
Source details

View original posting ↗

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Location & working pattern

London, England, United Kingdom

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