Financial Crime Data Scientist at AppGate Cybersecurity, Inc.

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

02 May, 26

Salary

175000.0

Posted On

01 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Fraud Detection, Machine Learning, SQL, Python, Data Visualization, Statistical Analysis, Anomaly Detection, Data Engineering, Automation, Investigative Skills, Collaboration, Risk Assessment, Fraud Intelligence, Cloud Platforms, Big Data Technologies

Industry

Computer and Network Security

Description

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.

Responsibilities

The Financial Crime Data Scientist will conduct investigations into financial crime activities and analyze various data signals to detect fraud. They will also collaborate with product teams to integrate intelligence into fraud security products.