Alexander Barnes
Financial Crime Product - Data Scientists & Engineers
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Financial Crime Product - Data Scientists & Engineers
Alexander Barnes are partnered with a high-growth fintech building out its financial crime product capability across fraud, transaction monitoring, and screening.
These roles sit within product and engineering, in the first line. You’ll either be defining how detection works, or building the systems it runs on.
What you’ll be doing
- Building and improving detection systems across fraud and AML (card, banking, TM, screening)
- Working directly with transaction, auth, behavioural, and network data to identify patterns and signals
- Developing detection logic across rules, models, and AI
- Tuning systems continuously. False positives, detection coverage, operational load, customer impact
- Designing features and intelligence layers that improve how risk is detected
- Running deep analysis in SQL and Python. No reliance on dashboards
- Translating typologies into production-ready signals and decisioning logic
- Deploying and scaling models and rules in real-time systems
- Partnering closely with product, engineering, and compliance to evolve detection frameworks
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Where this role can sit
Depending on your background, this leans into one of the following:
Fraud Risk (Card / Banking)
- CNP, ATO, scams, APP, mule detection, onboarding abuse
- Working with auth data, payment flows, chargebacks, behavioural signals
Transaction Monitoring (AML)
- Owning system performance. Rule effectiveness, typology coverage, backlog, false positives
- Working closely with ML models and monitoring frameworks
Screening (Sanctions / PEP)
- Match quality, list coverage, tuning logic, global screening performance
Detection Engineering / ML Systems
- Building monitoring frameworks from scratch
- Deploying models into production
- Scaling decisioning systems across large transaction volumes
What we’re looking for
- Hands-on experience in fraud, AML, or financial crime risk
- Strong understanding of typologies and how they translate into detection logic
- Strong SQL. Complex queries, large datasets, no hand-holding
- Python for analysis and modelling (pandas, numpy; ML exposure expected)
- Experience building, tuning, or deploying detection systems (rules and/or models)
- Ability to think about systems end-to-end. Not just models, but performance and outcomes
- Comfortable working across product, engineering, and compliance
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Profiles that tend to work
- Fraud / risk data scientists from issuers, fintechs, or banks
- TM or screening specialists who understand system performance, not just policy
- Engineers who’ve built risk or monitoring systems at scale
- Investigators or law-side profiles who’ve moved into detection and pattern analysis
What doesn’t work
- Ops-only or case handling backgrounds
- Compliance or policy profiles without data or system ownership
- Engineers with no exposure to financial crime or risk systems
- People who can’t show what they’ve built, tuned, or improved
Why this role exists
Most teams measure financial crime after it happens. This team is building the systems that detect it earlier, adapt faster, and scale properly.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location