SEON is the command center for fraud prevention and AML compliance, helping thousands of companies worldwide stop fraud, reduce risk and protect revenue. Powered by 900+ real-time, first-party data signals, SEON enriches customer profiles, flags suspicious behavior and streamlines compliance workflows - all from one place. SEON provides richer data, more flexible and transparent analysis, and faster time to value than any other provider on the market. We’ve helped companies reduce fraud by 95% and achieve 32x ROI, and we’re growing fast, thanks to our partnerships with some of the world’s most ambitious digital brands like Revolut, Wise, and Bilt.
We're forming a new AML Search Quality team to improve the accuracy of our AML screening, and grow our data science capability in this domain. As a Senior AI & ML Engineer, you'll join a cross-functional team that combines two skill sets:
Data scientists improving the accuracy of AML name matching, including fuzzy search scoring, configuration tuning, and match quality evaluation across sanctions, PEP, and adverse media data
Engineers building and operating the fuzzy search services that apply these models and configurations to customer screening in production
Your mission is to improve the accuracy of SEON's AML name screening, with fewer missed true matches and fewer false positives, and to make that accuracy measurable. You'll own modeling and experimentation for the team's data science work, from problem definition to production, working closely with engineers, and help shape the team's technical direction in an area where accuracy directly affects our customers' compliance outcomes.
This role can be based remotely within the EU.
WHAT YOU’LL DO:
Drive modeling and experimentation for SEON's fuzzy matching and AML screening quality, turning ambiguous problems into well-defined approaches and carrying them from experiment through release and operation
Build evaluation datasets and metrics, using offline and online signals, to measure match quality and guide model and configuration changes
Design scoring and ML approaches that fit real-time decisioning constraints, balancing match quality, performance, cost, and operational overhead
Own the production quality of your models and configurations, including monitoring for data and concept drift, on-call participation, and incident follow-up
Investigate customer-reported matching issues together with Product and Customer Success, and keep the customer voice in prioritization
Collaborate with Product, QA, Architects, DevOps, and Customer Success
Capture learnings from experiments so they get reused, and introduce AI tools and techniques where they provide clear value
Mentor teammates and raise the bar for data science practice across the team
WHAT YOU’LL BRING:
5+ years of experience in data science or machine learning, delivering models to production
Strong Python skills
Strong command of statistical analysis and experiment design, including offline and online evaluation of search or matching quality (for example precision and recall trade-offs)
Experience with NLP or text-based machine learning, such as text classification, named entity recognition (NER), or text matching
Experience owning the full lifecycle of data-driven systems, from data and experimentation to deployment and monitoring, anticipating data drift, leakage, and bias
Solid background in data analysis and SQL, with a working understanding of backend services and APIs
Experience with non-relational or search databases, ideally Elasticsearch
Working experience with a major cloud provider (ideally AWS), including deploying models and data pipelines on it
Clear communication: you can explain modeling trade-offs to Product and non-technical stakeholders
A track record of turning ambiguous problems into end-to-end solutions, and of spotting delivery risks early and raising them with a proposed mitigation
AMAZING IF YOU ALSO HAVE:
Prior experience in AML, KYC, or another regulated financial services domain
Experience driving cross-team technical work
Experience using AI-assisted development tools and helping a team apply them with good judgment
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SEON is an equal opportunity employer. We strive to embrace what makes each one of us unique; we each have our own story. Whether looking at our current staff or future team members, we believe that everyone has something to contribute, and our employment practices reflect that. We do not make an employment decision based upon race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Please let your recruiter know if you need reasonable adjustments to our recruitment process.