Job Overview

The Financial Crime Data Scientist combines investigative expertise with advanced data science techniques to identify, assess, and mitigate financial risks, fraud, and emerging cyber-enabled threats. This role will analyze transactional, behavioral, and device-level signals; detect indicators of compromise; identify anomalous activity; and support intelligence integration into Appgate's Fraud security products.

The analyst will collaborate closely with internal product teams and other external intelligence sources to track financial crime patterns (e.g., ransomware operators, malware families, account takeover trends, money mule networks) and translate insights into predictive models, detection rules, and automated workflows.

This candidate bridges fraud investigation, data analysis, and technical implementation while working cross-functionally with Product, Engineering, Risk, Marketing, and Operations.

This is an on-site role with 5 days required in our NY office.

Responsibilities

Fraud & Threat Intelligence

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

Data Analytics & Modeling

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

Data Engineering & Automation

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

Cross-Functional Intelligence Integration

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

Security & Compliance

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

Required Qualifications

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
- Python (pandas, NumPy, scikit-learn) for data analysis
- 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 / Nice-to-Have Technical Skills

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

Key Competencies

Analytical and critical thinking
Statistical and machine learning literacy
Effective communication and storytelling with data
Investigative rigor and attention to detail
Cross-functional collaboration
Integrity and confidentiality
Strong problem-solving and decision-making skills

Requirements:

Position is on-site in our NYC office.
Willing to relocate if not local to NYC
No visa sponsorships are available at the moment
Compensation Range: 140-175k base + bonus

AppGate is An Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age or any other federally protected class. In furtherance of AppGate's policy regarding affirmative action and equal employment opportunity, AppGate has developed a written affirmative action program. This program is available for review upon request by any applicant or employee during normal business hours by contacting the company's EEO Coordinator.

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Financial Crime Data Scientist