Director, Data and Analytics Engineer

Morgan Stanley•Jersey City, NJ

1d•Hybrid

About The Position

Morgan Stanley Services Group, Inc. is seeking a Director, Data and Analytics Engineer in Jersey City, NJ to identify fraud trends, investigate suspicious behavior and identify significant insights from different sets of data. Work with data to detect out-of-pattern money movement transactions, such as, but not limited to debit cards, Wire Transfers, and trading. Combine internal data with external intelligence to detect fraud as early in the fraud lifecycle as possible. Create solutions to the trends identified by improving existing or creating new controls. Create effective solutions in improving efficiency using different analytical techniques and forecasting. Create rules in the rule's engine and conduct pre- and post-production testing to ensure effectiveness. Partner with the Data Science team to develop fraud risk models. Use statistical methods to analyze data and generate useful business reports. Communicate trends, findings, proposals and solutions to management and stakeholders. Measure performance using appropriate tools and techniques. Generate suspicious activity reports and risk management reports for management. Recommend or introduce new tools used for fraud detection, prevention and reporting activities. Creation of fraud strategy design for new product launches by defining risk thresholds. Telecommuting permitted up to Two (2) days per week.

Requirements

Requires a Bachelor’s in Economics, Data Science, Business Analytics or a related field
Requires three (3) years of experience in the position offered or three (3) years as an Associate, Senior Consultant, or a related role.
Requires three (3) years of experience with Automated Tableau dashboard creation for real-time fraud strategy monitoring
Requires three (3) years of experience with PySpark
Requires three (3) years of experience with Spark
Requires three (3) years of experience with Hadoop ecosystems
Requires three (3) years of experience with Root cause analysis and detection of fraud reversal patterns in payment systems
Requires three (3) years of experience with Automation of batch data workflows using shell scripting
Requires three (3) years of experience with Stakeholder management for cross-functional fraud risk initiatives
Requires three (3) years of experience with Fraud risk management across banking products, including payment systems, transaction monitoring, and retail lending
Requires three (3) years of experience with Python
Requires three (3) years of experience with MS Excel
Requires three (3) years of experience with Tableau visualizations for KPI tracking and strategy creation in fraud management
Requires three (3) years of experience with SAS programming for risk reporting in fraud analytics
Requires three (3) years of experience with SQL query design for fraud data aggregation and transformation.
Requires two (2) years of experience with Application fraud detection leveraging third-party fraud scores and custom rule engines
Requires two (2) years of experience with End-to-end model monitoring using AUC, KS, ROC, Decile plots, and Gain charts for fraud analytics
Requires two (2) years of experience with Development and tracking of fraud Key Risk Indicators (KRIs) for retail lending portfolios
Requires two (2) years of experience with Integration and interpretation of third-party fraud scores including Experian FraudNet, EWS, and Sentilink.

Responsibilities

Identify fraud trends, investigate suspicious behavior and identify significant insights from different sets of data.
Detect out-of-pattern money movement transactions, such as, but not limited to debit cards, Wire Transfers, and trading.
Combine internal data with external intelligence to detect fraud as early in the fraud lifecycle as possible.
Create solutions to the trends identified by improving existing or creating new controls.
Create effective solutions in improving efficiency using different analytical techniques and forecasting.
Create rules in the rule's engine and conduct pre- and post-production testing to ensure effectiveness.
Partner with the Data Science team to develop fraud risk models.
Use statistical methods to analyze data and generate useful business reports.
Communicate trends, findings, proposals and solutions to management and stakeholders.
Measure performance using appropriate tools and techniques.
Generate suspicious activity reports and risk management reports for management.
Recommend or introduce new tools used for fraud detection, prevention and reporting activities.
Creation of fraud strategy design for new product launches by defining risk thresholds.

Benefits

Medical
Prescription Drug
Dental
Vision
Health Savings Account
Dependent Day Care Savings Account
Life Insurance
Disability and Other Insurance Plans
Paid Time Off (including Sick Leave consistent with state and local law, Parental Leave and 20 Vacation Days annually)
10 Paid Holidays
401(k)
Short/Long Term Disability