Sumsub

anti-fraud analyst

In short

Senior anti-fraud analyst role at Sumsub, a SaaS company building AI-powered trust infrastructure for compliance, identity verification, and fraud prevention. The job focuses on exploring data for fraud patterns, building and tuning detection models (rules, supervised/unsupervised ML, anomaly and link-based analysis), and analysing alert flows and traffic. Requires 4+ years of relevant experience, strong Python/SQL, hands-on experience with analytics tools like ClickHouse, Trino, Superset, dbt, or DataHub, and fluent English. Fully remote position.

About
Sumsub provides AI-powered trust infrastructure for compliance operations, connecting identity and business verification, fraud prevention, transaction monitoring, and risk workflows. Its platform helps teams reduce manual work and enter new markets.
Responsibilities
•Explore live data to identify emerging fraud patterns, anomalous activity, and gaps in existing detection logic
•Develop and tune fraud detection approaches using rules, supervised and unsupervised models, anomaly detection, and link-based analysis
•Evaluate fraud signals for accuracy, completeness, and predictive value, and identify improvements
•Analyse client traffic and alert flows, measure false positives and missed fraud, and recommend improvements
•Build and optimise baseline rule sets and proactive alerts for common and emerging fraud scenarios
•Turn analytical findings into product recommendations and support complex, data-intensive client cases when needed
Requirements
•4+ years of experience in anti-fraud, risk analytics, applied data science, or another analytical role involving complex data and anomaly detection
•Strong knowledge of exploratory data analysis and classical machine learning, including supervised and unsupervised approaches
•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
•Ability to identify data-quality issues, bias, leakage, overfitting, and misleading model results when working with raw and imperfect data
•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
•Fluent English
Conditions
•Flexible working hours; no fixed 9-to-5 schedule, provided work remains productive and aligned with the team
•Birthday holiday, 10 personal days annually, seven sick days without paperwork, and extra time off for Christmas and New Year
•Financial bonuses for marriage and welcoming a new baby
•In-person team offsites a few times a year, with trips fully covered
•Role-specific events are covered, with learning opportunities and personal development plans
•Work equipment and tools are provided

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