A hands-on role improving fraud detection accuracy and signal quality across Sumsub products, working with live production data: investigating fraud patterns, testing and tuning detection logic, and deciding which signals, rules and models reliably flag suspicious activity.
Explore live data to identify emerging fraud patterns, anomalous activity, and gaps in existing detection logic.
Build and refine fraud detection methods using rules, supervised and unsupervised models, anomaly detection and graph-based analysis.
Assess the accuracy, completeness and predictive power of fraud signals and flag which ones need fixing or improvement.
Analyse client traffic and alert flows, measure false positives and missed fraud, and recommend concrete improvements.
Build and optimise baseline rule sets and proactive alerts for common and emerging fraud scenarios.
Turn analytical findings into clear product recommendations and support complex, data-intensive client cases when needed.
Requirements
4+ years in anti-fraud, risk analytics, applied data science, or a similar analytical role involving anomaly detection.
Solid grounding in exploratory data analysis and classical machine learning, both supervised and unsupervised methods.
Practical experience with anomaly detection, clustering, and link-based or graph analysis.
Hands-on experience in fraud prevention or fraud analytics.
Strong Python and SQL skills.
Hands-on experience with ClickHouse, Trino, Superset, dbt, DataHub, or comparable analytics tools.
Good judgement with raw, imperfect data, including spotting data-quality problems, bias, leakage, overfitting and misleading model results.
Ability to evaluate detection quality through false positives, false negatives, and other relevant metrics.
Ability to explain complex findings through clear logic, numbers, and concrete examples.
Confident but critical use of AI-assisted analytical tools, including the ability to validate their outputs.
Fluency in English.
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Denidel's HR team submits and tracks the application on your behalf.
