Data Scientist Manager Job Listing at Valley Bank in Morristown, NJ (Job ID 25-637)

Description

Responsibilities include, but are not limited to:

• Lead development and deployment of Data Science projects by advising Data on technical matters and guiding them in problem formulation.
• Manage performance and development of Analytics Engineer(s).
• Lead the implementation of design, development, deployment, and maintenance of predictive/prescriptive/statistical models for Financial Crimes Compliance.
• Initiate and guide studies with the use of descriptive and supervised machine learning methods and advanced statistical methods using innovative and the latest advanced techniques and algorithms.
• Establish methodology and procedures for the tuning of the various models, including methodology for above and below the line tuning and sampling.
• Conduct the tuning and optimization of the various financial crime models based on the methodology established. Make recommendations to improve financial crime monitoring through the development of new risk models, statistical analysis of model thresholds, and other sensitivity and productivity analyses.
• Maintain control and governance documentation evidencing changes and approvals for updates to the financial crime models.
• Generate, monitor, and update machine learning models used to identify suspicious activity or enhance productivity. Work with Fraud and AML teams to identify new models to assist in the identification of suspicious behavior or to enhance productivity.
• Direct tuning efforts and priorities by measuring and monitoring the success of existing models, including, but not limited to customer risk scoring, transaction monitoring and segmentation models.
• Use advanced statistical methods like clustering and regression analysis to ensure appropriate rigor around optimization processes.
• Conceptualize and develop new rules/models/scenarios, in coordination with Fraud and AML subject matter experts, to address emerging trends and red flags.
• Ensure sound risk coverage, adequate quantitative model assessment and validation, and data quality completeness and integrity.
• Develop data-driven insights and communicate these effectively to management and diverse stakeholders, including management and regulators, using visualization techniques to showcase the results of analysis in concise presentations.
• Support the Financial Crimes Group's periodic risk assessments through the analysis of data elements related to potential indicators of customer, product, or geographic risk, evaluating and enhancing the Group's risk rating methodologies, and identifying new quantitative factors that can be incorporated into the risk assessment process.
• Support Model Risk Management's model validation efforts to ensure models are performing as intended.
• Conduct ad hoc analyses for FCC, company leadership and business partners, as needed.