Data Scientist I - Model Governance - Fraud Prevention and Detection

Bank of America•Fort Worth, TX

9d•Onsite

About The Position

Bank of America’s Fraud Prevention and Detection team is seeking a model validation professional to help combat financial crime. The role analyzes and interprets large datasets to uncover potential revenue opportunities and develop risk management strategies. It involves working with stakeholders to understand business problems, applying data-gathering and analytical techniques, and presenting recommendations based on findings. The position focuses on validating and monitoring internally and vendor-developed models, including models using artificial intelligence. The successful candidate will work with developers to understand model methodology and challenge it effectively, coordinate with technology teams and model users on deployments, and partner with independent model risk management, legal, and compliance teams to obtain model approval. The role also produces analytics to assess model results, monitors ongoing performance, tracks post-deployment changes, and supports governance requirements and risk mitigation. Bank of America describes an in-office culture supporting collaboration, engagement, and career development, with flexibility based on role-specific responsibilities and business needs. The position is first shift and scheduled for 40 hours per week.

Requirements

Undergraduate or graduate quantitative degree, or relevant certifications and experience.
At least 3 years of experience in model validation or development.
Experience with model governance, model documentation, model monitoring, and interactions with model risk management functions.
Strong analytical skills, including investigating performance trends, identifying root causes, and recommending solutions.
Proficiency with SQL and SAS.
Excellent technical writing skills.
Critical problem-solving abilities, including selecting data and deploying solutions.
Excellent communication and influencing skills.
Ability to thrive in a fast-paced, highly dynamic environment.
Intellectual curiosity and a strong desire to investigate the causes of problems and develop creative solutions.

Nice To Haves

Advanced quantitative degree (Master’s or PhD).
5 or more years of experience working in model validation.
Financial services experience; familiarity with SR-26-2 guidance is highly desirable.
Working knowledge of Python.

Responsibilities

Perform business analytics, including data analysis, trend identification, and pattern recognition using techniques such as machine learning, text mining, and statistical analysis.
Apply agile practices to project management, solution development, deployment, and maintenance.
Create and maintain technical documentation that captures business requirements and specifications for analytical solutions and their production implementation.
Manage multiple priorities and deliver quality, timely quantitative models, data science products, analysis reports, and visualizations, both independently and in a team environment.
Present technical concepts and analysis results to diverse internal stakeholders and build professional relationships that foster collaboration.
Identify potential issues and develop controls to mitigate risk.
Research advances in data science and artificial intelligence to support business analytics.
Develop advanced technical documentation for internally and vendor-developed models.
Work with developers to understand models, challenge methodologies, and ensure results are accurate.
Partner with technology teams and model users to schedule deployments and plan future model installations.
Work with independent model risk management, legal, and compliance teams to ensure models are validated and approved for use.
Produce analytics to check whether early model results are consistent with expectations.
Conduct regular model monitoring and share performance results and analytical insights with model stakeholders and users.
Support Bank policy and governance requirements for artificial intelligence models and identify and mitigate risks associated with advanced techniques.
Track model changes after deployment and ensure documentation reflects adjustments, patches, and updates.
Perform model performance analytics beyond Model Risk Management policy requirements, including granular monitoring, early trend detection, root cause analysis, and gap analysis.