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
Department: Growth
From the employer’s posting
What you’ll work on
Full postingDesign, 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 qualificationsCore 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
Report an error- 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
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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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
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
London, England, United Kingdom
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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