AppGate
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.
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 productionise 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 visualisations 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 visualisation tools (Tableau, Power BI, Looker, etc.)
Familiarity with machine learning concepts, anomaly detection, statistics, and predictive modelling
Experience with fraud platforms, case management systems, device intelligence, or behavioural 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, behavioural 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
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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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.
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 productionise 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 visualisations 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 visualisation tools (Tableau, Power BI, Looker, etc.)
Familiarity with machine learning concepts, anomaly detection, statistics, and predictive modelling
Experience with fraud platforms, case management systems, device intelligence, or behavioural 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, behavioural 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
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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