Principal Data Scientist - Risk Data Science

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

As a Principal Data Scientist, you will work alongside Senior and Principal Data Scientists and subject matter experts to support Anti Money Laundering (AML) and Sanctions vendor models. You will collect, organize, and prepare data for use in the development of models to support these areas as well as other areas of risk including consumer risk .

The Principal Data Scientist will be responsible for:

• Supporting and maintaining vendor models used by the AML group for transaction monitoring and sanctions screening.
• Applying appropriate quantitative techniques such as exploratory data analysis, regression analysis, supervised and unsupervised learning, feature engineering, statistical sampling, and data visualization
• Utilizing traditional statistical methodologies as well as innovative techniques from the machine learning and data engineering fields
• Producing and articulating strategic recommendations based on rigorous data analysis
• Developing and supporting implementation of both internal and vendor developed models, using machine learning (ML), statistical, and other quantitative methods
• Building relationships with the business line and collaborating cross-functionally to access internal as well as industry data sources, understand the data being analyzed, and identify process improvement opportunities
• Utilizing various data mining techniques and methodologies to produce analytical solutions required to assist business stakeholders
• Preparing model documentation and responding in a timely manner to effective challenge from model validation, internal audit, and regulatory reviews; overseeing the tracking and execution of all external and internal validation findings related to vendor and internally developed models
• Supporting ad-hoc requests from line of business by understanding, communicating, and managing model risks
• Developing and leveraging knowledge of AML & Sanctions risks and controls as well as consumer credit and other areas of banking to lead input analysis, fine tuning, calibration of the models and identify opportunities for process optimization

Qualifications:

• 5-6 years of work experience in data modeling, statistical analysis, machine learning and relevant computing requirements
• Experience in producing documentation in a highly regulated environment 
• Proven experience working with model risk management in the model validation process.
• Strong verbal communication skills required with an ability to successfully communicate analytic results, business insights and resulting business implications to non-technical business partners
• Ability to work in a team environment and collaborate with colleagues who have a background in statistics, database development/maintenance, or information technology 
• Strong programming skills and ability to utilize a variety of data/analytic software languages/tools (e.g., Python, SQL, R, etc.)
• Deep knowledge of a variety of statistical concepts and procedures, such as: generalized linear models, machine learning algorithms, experimental design, stochastic processes, and data visualization
• Experience supporting AML & Sanctions and/or consumer banking preferred.

Education:

• Master's Degree in Operations Research/Statistics/Mathematics/Physics/Computer Science/Economics required
• PhD preferred 

Hours and Work Schedule: 3 days in the office, 2 remote

Hours per Week: 40

Work Schedule: Monday-Friday