Lead Data Scientist – Gen AI Lead

Jobtailor; New Jersey; On-site; USD 150,000 - 210,000; Full time

Job summary

Jobtailor in New Jersey seeks a senior data scientist to analyze large fraud datasets and develop ML models that enhance detection. You will work with cross-functional teams to translate business needs into analytical solutions and actionable recommendations.

The role emphasizes Python/R, SQL, and ML techniques, ongoing monitoring, and collaboration with fraud operations to keep models and rules current within AWS environments.

Qualifications

• 5+ years of hands-on data science/analytics experience
• Strong Python or R for analysis and modeling
• SQL and experience with large-scale datasets
• Experience with statistical modeling and ML: classification, regression, clustering, anomaly detection
• Ability to collaborate across operations and technology teams
• Proven ability to learn new domains quickly and independently
• Familiarity with model evaluation metrics for imbalanced classification (precision, recall, AUC, F1)

Responsibilities

• Analyze large, complex fraud datasets to identify patterns, trends, and anomalies for detection strategies
• Evaluate rule-based fraud systems and deliver recommendations for updates or new rules
• Partner with fraud operations teams to translate insights into hypotheses and solutions
• Prototype and contribute to agentic AI applications using AWS tools
• Collaborate with fraud tech teams to ensure models and rules are implemented and monitored in AWS production
• Design, build, and validate ML and statistical models to improve detection precision/recall and reduce false positives
• Monitor deployed models and rules for performance and gaps and intervene as needed
• Communicate findings and strategic recommendations to both technical and non-technical stakeholders
• Stay current with fraud trends, financial crime, and AI/ML developments in AWS ecosystem

Skills

Python Proficiency

SQL Expertise

Statistical Modeling

Machine Learning

Data Analysis

Anomaly Detection

Education

Bachelor's Degree

Tools

Amazon Q

Kiro

AWS AgentCore

AI-Assisted Development Tools

AWS Production Environment

Job description

• Analyze large, complex fraud datasets to identify patterns, trends, and anomalies that inform detection strategies and business decisions
• Evaluate existing static, rule-based fraud detection systems through data-driven assessments of their performance and coverage, and deliver clear, prioritized recommendations for rule updates, retirement, or new rule creation
• Partner with fraud operations teams to understand frontline detection challenges and translate operational insights into analytical hypotheses and actionable solutions
• Utilize AI-assisted development tools such as Amazon Q and Kiro to accelerate analytical workflows and solution delivery
• Prototype and contribute to the development of agentic AI applications leveraging AWS AgentCore and generative AI solutions that advance the team's fraud strategy capabilities
• Collaborate with fraud technology teams to ensure models, rules, and AI-driven outputs are implemented accurately and monitored effectively within the AWS production environment
• Design, build, and validate machine learning and statistical models to enhance fraud detection capabilities, improve precision and recall, and reduce false positive rates
• Monitor deployed models and fraud rules on an ongoing basis, identifying performance degradation or emerging detection gaps that require intervention
• Communicate findings, model results, and strategic recommendations clearly to both technical and non-technical stakeholders
• Stay current with emerging trends in fraud typologies, financial crime, and AI and machine learning developments within the AWS ecosystem, and bring relevant innovations back to the team

Requirements

• University (Degree) Preferred
• 5+ Years Required; 7+ Years Preferred
• 5+ years of hands-on experience in data science, analytics, or a closely related quantitative discipline
• Strong proficiency in Python or R for statistical analysis and model development
• Solid command of SQL and experience working with large-scale structured and unstructured datasets
• Demonstrated expertise in statistical modeling and machine learning techniques including classification, regression, clustering, and anomaly detection
• Ability to manage relationships across multiple stakeholder groups including operations and technology teams
• Proven ability to learn new domains, tools, and methodologies quickly and independently
• Solid understanding of model evaluation metrics for imbalanced classification problems (e.g., precision, recall, AUC, F1)

Core Competencies

Expertise in analyzing complex fraud datasets and developing machine learning models to enhance fraud detection capabilities. Proficient in utilizing AI-assisted tools and collaborating with cross-functional teams to implement effective fraud strategies.

Highest-signal resume keywords

• Python Proficiency
• SQL Expertise
• Statistical Modeling
• Machine Learning Techniques
• Data Analysis Experience

ATS Optimization Keywords

Hard Skills

• Data Science
• Statistical Analysis
• Model Development
• Anomaly Detection
• Classification Techniques
• Regression Techniques
• Clustering Techniques
• Model Evaluation Metrics
• Fraud Detection
• Data-Driven Assessments

Soft Skills

• Stakeholder Management
• Communication Skills
• Analytical Thinking
• Problem Solving
• Collaboration

Industry Keywords

• Fraud Detection
• Financial Crime
• Emerging Trends
• AI Developments
• Machine Learning

Tools & Technologies

• Amazon Q
• Kiro
• AWS AgentCore
• AI-Assisted Development Tools
• AWS Production Environment