Lead data and feature engineering efforts to extract, transform, and prepare high-quality data inputs for fraud model development, focusing on identifying key attributes that drive accurate fraud detection
Build predictive models and machine-learning and AI algorithms with large amounts of structured and unstructured data
Ownership and management of fraud models, risk appetite execution and defect analysis
Design, develop, and implement advanced machine learning models to detect and prevent fraud across the entire lifecycle, including application fraud, synthetic ID fraud, account takeover, and evolving attack schemes
Utilize advanced data processing techniques to manage large, complex datasets, including data cleaning, normalization, and augmentation, ensuring robust model performance
Conduct comprehensive exploratory data analysis (EDA) to uncover hidden patterns, trends, and anomalies that can inform model development and feature engineering
Collaborate closely with technology teams, fraud analytics, and business partners to align on data strategies, stay updated on industry trends, and proactively identify potential and existing fraud risks
Continuously optimize and refine fraud models through feature selection, hyperparameter tuning, and ongoing performance monitoring, ensuring models remain adaptive to new fraud tactics
Support model deployment and integration into production systems, ensuring seamless real-time fraud detection and efficient feedback loops for continuous model improvement
Evaluate and select appropriate machine learning algorithms and tools based on specific fraud detection needs and data characteristics
Engage in cross-functional initiatives to enhance data quality and governance, improving overall fraud prevention capabilities
Participate in model validation and testing processes to ensure compliance with regulatory standards and alignment with best practices in fraud risk management
Generate and manage regular and ad-hoc reporting to enable effective monitoring and identification of emerging trends

Requirements

Bachelor’s Degree required in statistics, mathematics, physics, economics, or other analytical or quantitative discipline
Master's Degree or PhD preferred
5+ years in data science, machine learning, or advanced analytics
Strong Technical Skills Proficiency in programming languages such as Python, R, or SQL for data manipulation, feature engineering, and model development
Strong experience with data processing tools and libraries (e.g., Pandas, Numpy, PySpark) for handling large and complex datasets
Deep understanding of machine learning algorithms (e.g., decision trees, gradient boosting, neural networks, natural language processing) and statistical modeling techniques used for fraud detection
Expertise in feature engineering, including creating, selecting, and refining features to improve model accuracy and performance
Data Engineering: Experience with building and optimizing data pipelines, ETL processes, and real-time data streaming for fraud detection solutions
Machine Learning Operations: Familiarity with model development, monitoring, and versioning in production environments
Analytics Skills: Strong ability to conduct exploratory data analysis (EDA) and identify actionable insights from large datasets to drive model development
Collaboration: Proven track record of working cross-functionally with technology, analytics, and business teams to implement and optimize fraud prevention strategies
Communication: Ability to translate complex technical findings into clear, actionable insights for non-technical stakeholders and business leaders
Problem-Solving: Strong problem-solving skills with the ability to think critically and creatively in a fast-paced environment
Regulatory Compliance: Familiarity with regulatory requirements and best practices related to fraud modeling and risk management
Multi-Tasking and Deadline Management: Demonstrated ability to manage multiple projects and priorities simultaneously while meeting tight deadlines
Attention to Detail: High level of attention to detail and precision in data analysis, model development, and reporting
Intellectual Curiosity: Strong intellectual curiosity and eagerness to stay updated with the latest developments in data science, machine learning, and fraud detection techniques

Benefits

medical, dental & vision coverage
401(k)
life, accident, and disability insurance
wellness programs
paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringfeature engineeringmachine learningpredictive modelingexploratory data analysisstatistical modelingdata manipulationETL processesreal-time data streamingfraud detection

Soft Skills

collaborationcommunicationproblem-solvingattention to detailmulti-taskingdeadline managementintellectual curiosity