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About the Role

This role involves reviewing suspicious activity and complex fraud cases to identify and resolve fraud risk trends. Responsibilities include documenting investigation findings, writing rules in LexisNexis decision engines, managing customer risk policy, and performing offline analyses of customer data to tune rules, expose patterns, and reduce false positives. The role also requires identifying insights from chargeback data, interpreting findings for stakeholders, and analyzing high-volume data to report fraudulent transaction trends.

Responsibilities

Review suspicious activity and complex fraud cases to identify and resolve fraud risk trends and issues.
Document investigation findings and conclusions thoroughly.
Write rules in LexisNexis decision engines and manage customer risk policy.
Leverage hundreds of signals in LexisNexis Digital Identity Network and customer-fed data.
Perform offline analyses of customer data to tune rules, expose patterns, research anomalies, and reduce false positives.
Build executive and project-level reports.
Identify meaningful insights from chargeback data.
Interpret and communicate analysis findings to engineers, product, and stakeholders.
Analyze high-volume data to investigate, identify, and report trends linked to fraudulent transactions.
Collaborate with LexisNexis teams including Products, Engineering, and colleagues to enhance tools, data sources, system functionalities, and fraud detection methods.
Educate internal team members and external parties on processes and procedures.
Demonstrate a professional and customer-centric persona when interacting with merchants and customers via on-site visits, phone, e-mail, and chat.
Provide Risk and Technology consulting, sharing best practices for combating persistent and emerging security threats.
Perform other duties as needed.

Requirements

Master’s degree (or foreign equivalent) in Data Science, Computer Science, Business Analytics, Information Systems, or a related field.
3 years of experience in the job offered or related occupations.
3 years of experience in a technical suite, including Python (numpy, Pandas, sci-kit learn), SQL, cloud services (Snowflake), Jupyter Notebooks, collaboration tools (Git, BitBucket), visualization tools (Matplotlib, Tableau, PowerBI), and IDE (Visual Studio Code).
3 years of experience with machine learning modeling, overall modeling pipeline, and Feature Engineering, including supervised learning (logistic regression, XG Boost, LGBM), anomaly detection, clustering, graph analysis, feature engineering, data preprocessing, performance metrics (accuracy, precision, recall, F1, AUC-ROC, AUC-PR), and model governance documentation.
3 years of experience clearly and effectively communicating complex data insights and analysis results to clients who may not have a technical background.
3 years of experience applying analytical thinking to understand client issues and propose tailored solutions leveraging company solutions and capabilities to prevent fraud and provide a seamless customer experience.
3 years of experience understanding various types of fraud (e.g., credit card fraud, identity theft, phishing, stolen credentials fraud, first party fraud), familiarity with industry-specific fraud schemes, and awareness of emerging fraud trends and techniques.
3 years of experience conducting thorough risk evaluations to determine the impact, likelihood, and scope of potential fraud incidents.
3 years of experience using data analysis to identify patterns or anomalies that might indicate potential risks.
3 years of experience understanding how to balance fraud prevention measures with the need for a positive user experience.

Skills

Python (numpy, Pandas, sci-kit learn)
SQL
Cloud services (Snowflake)
Jupyter Notebooks
Git
BitBucket
Matplotlib
Tableau
PowerBI
Visual Studio Code
Machine learning modeling
Modeling pipeline management
Feature Engineering
Supervised learning (logistic regression, XG Boost, LGBM)
Anomaly detection
Clustering
Graph analysis
Data preprocessing
Performance metrics (accuracy, precision, recall, F1, AUC-ROC, AUC-PR)
Model governance documentation
Analytical thinking
Data analysis
Fraud prevention
Risk evaluation
Communication of complex data insights

Location

521 Fifth Avenue, 7th Floor, New York, NY 10175
New York, NY (office)
Telecommute from any location within the U.S.

Experience Level

3 years of experience

Education Level

Master’s degree (or foreign equivalent) in Data Science, Computer Science, Business Analytics, Information Systems, or a related field

Salary/Compensations

$156,409.90 to $184,600/year

Benefits

Standard company benefits

About the Company

RELX is a global provider of information-based analytics and decision tools for professional and business customers.
RELX enables customers to make better decisions, get better results, and be more productive.
RELX's purpose is to benefit society by developing products that help researchers advance scientific knowledge, doctors and nurses improve patient lives, lawyers promote the rule of law, businesses and governments prevent fraud, consumers access financial services, and customers learn about markets.
RELX's purpose defines the company and guides actions beyond product development.
RELX employees are inspired to undertake initiatives that make unique contributions to society and the communities in which they operate.

Equal Opportunity

Committed to providing a fair and accessible hiring process.
Accommodation or adjustment available for disabilities or other needs via Applicant Request Support Form or contact 1-855-833-5120.
An equal opportunity employer: qualified applicants are considered and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.
USA Job Seekers: EEO Know Your Rights.