Data Scientist II - Consumer Client Protection

Bank of America•Tampa, FL

1d•Onsite

About The Position

This job is responsible for reviewing and interpretating large datasets to uncover revenue generation opportunities and ensuring the development of effective risk management strategies. Key responsibilities include working with lines of business to comprehend problems, utilizing sophisticated analytics and deploying advanced techniques to devise solutions, and presenting recommendations based on findings. Job expectations include demonstrating leadership, resilience, accountability, a disciplined approach, and a commitment to fostering responsible growth for the enterprise. Fraud Prevention and Detection is looking for an experienced model validation professional to join our team and help us combat financial crime.

Requirements

4+ years of experience in model validation and/or development is required
Must be proficient with SQL and SAS
Excellent technical writing skills
Critical problem solving abilities including selection of data and deployment of solutions
Proven ability to manage projects, exercise thought leadership and work with limited direction on complex problems to achieve project goals while also working as part of a broader team
Excellent communication and influencing skills
Thrives in fast-paced and highly dynamic environment
Intellectual curiosity and strong urge to figure out the “whys” of a problem and come up with creative solutions

Nice To Haves

Advanced Quantitative degree (Masters or PhD)
7+ years of experience working in model validation.
Experience in financial services (following SR-11-7 guidance is very desirable)
Proficiency with Python and Tableau

Responsibilities

Enables business analytics, including data analysis, trend identification, and pattern recognition, using advanced techniques to drive decision making and collection data driven insights
Applies agile practices for project management, solution development, deployment, and maintenance
Develops and reviews technical documentation, capturing the business requirements, and specifications related to the developed analytical solution and implementation in production
Manages multiple priorities and ensures quality and timeliness of work deliverables such as quantitative models, data science products, data analysis reports, or data visualizations, while exhibiting the ability to work independently and in a team environment
Delivers presentations in an engaging and effective manner through in-person and virtual conversations that communicates technical concepts and analysis results to a diverse set of internal stakeholders, and develops professional relationships to foster collaboration on work deliverables
Supports the identification of potential issues and development of controls
Maintains knowledge of the latest advances in the fields of data science and artificial intelligence to support business analytics
Developing advanced technical documentation for an array of internally- and vendor-developed models ranging from regression to sophisticated techniques including XGB and neural networks
Working closely with developers to understand how the model works and provide effective challenge to not only push back on methodology but also ensure results are accurate
Partnering with technology and model users to schedule deployments and planning ahead for future model installations
Working with independent model risk management, legal and compliance teams to ensure models are fully validated and approved for usage
Producing analytics to ensure early model results look consistent with expectations
Conducting regular model monitoring and sharing performance results and analytical insights with model stakeholders and users
Supporting Bank policy for Artificial Intelligence models and ensuring any risks of using advanced techniques are identified and mitigated
Tracking model changes after deployment and ensure appropriate documentation reflects any adjustments, patches or updates
Driving model performance analytics above and beyond Model Risk Management policy requirements, including granular performance monitoring, early trend detection, root cause analysis, and gap analysis