AppGate · 1 day ago
Financial Crime Data Scientist
New York, NY
Full-time
Onsite
Entry, Mid Level
$140K/yr - $175K/yr
1+ years expAppGate is a company focused on cybersecurity and fraud prevention, and they are seeking a Financial Crime Data Scientist. This role involves combining investigative expertise with data science techniques to identify and mitigate financial risks and fraud, while collaborating with various teams to enhance fraud detection capabilities.
Cloud SecurityCyber SecurityNetwork SecuritySoftware
No H1B
Responsibilities
Conduct in-depth investigations into financial crime activity, including transaction fraud, account compromise, synthetic identity, malware-enabled fraud, and ransomware monetization patterns
Monitor intelligence feeds for emerging threat actors, TTPs, botnet activity, phishing kits, malware variants, and monetization schemes
Identify fraud indicators, behavioral patterns, anomalies, and signal correlations across structured and unstructured data sources
Collect, clean, engineer, and analyze large datasets using Python, SQL, and cloud-based data platforms
Perform statistical analysis, clustering, anomaly detection, and supervised/unsupervised machine learning to improve predictive fraud scoring
Build prototypes for fraud detection algorithms; partner with data science teams to productionize models
Build and maintain analytical data pipelines with engineers using tools such as Airflow, dbt, Spark, or similar
Automate data ingestion (APIs, logs, intelligence feeds, enrichment sources) for ongoing fraud monitoring
Create dashboards and visualizations using Tableau, Power BI, Looker, Mode, or similar to communicate findings
Translate fraud intelligence into actionable requirements for product and engineering teams (e.g., detection rules, model features, new risk signals)
Collaborate with marketing and customer-facing teams to prepare intelligence briefs, threat summaries, and fraud trend reports
Produce fraud loss metrics, risk scoring insights, and performance evaluations of prevention tools
Maintain strict confidentiality and follow handling protocols for sensitive data, PII, and regulated financial information
Stay current on fraud trends, sanctions, AML regulations, and industry standards
Qualification
Required
Bachelors/Masters degree in Data Science, Applied Statistics, Digital Forensics, Financial Engineering, Criminology, Computer Science, Cybersecurity, or relevant field; or equivalent experience
1-3+ years in fraud detection, threat intelligence, financial crime investigations, cyber threat analysis, or risk operations
Strong proficiency in: SQL for data extraction and manipulation
Strong proficiency in: Python (pandas, NumPy, scikit-learn) for data analysis
Strong proficiency in: Data visualization tools (Tableau, Power BI, Looker, etc.)
Familiarity with machine learning concepts, anomaly detection, statistics, and predictive modeling
Experience with fraud platforms, case management systems, device intelligence, or behavioral analytics systems
Demonstrated investigative mindset with excellent documentation and communication skills
Preferred
Experience with big data technologies (Spark, Databricks, Snowflake)
Knowledge of fraud-specific data sources: device fingerprinting, behavioral biometrics, geolocation, IP intelligence, OSINT, malware intel feeds
Familiarity with malware families, attack chains, and cyber threat intelligence frameworks such as MITRE ATT&CK
Exposure to API-based integrations, data enrichment pipelines, and log analysis
Understanding of risk scoring systems, rules engines, or real-time decisioning platforms
Experience with AML, KYC, BSA, sanctions screening, or cryptocurrency tracing tools
Company
AppGate
AppGate develops and provides cloud and hybrid-ready security and analytics products and services.
501-1000 employees
Funding
Current Stage
Late StageLeadership Team
Kurt Glazemakers
Chief Technology Officer
Tina Gravel
SVP Global Channels and Alliances
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