Data Scientist Principal – Bank AI/ML

Data Scientist Principal – Bank AI/ML

Requirements

• Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline.
• 10 years of progressive experience in predictive analytics or data analysis, to include 6 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
• 6 years of experience in one or more dynamic scripted language (such as Python, R, etc.) with a focus on writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
• Subject matter expert in the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic models, discriminant analysis, support vector machines, decision trees, forest models, etc.
• Subject matter expert in the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
• Deep hands-on experience building, deploying, and managing the performance of advanced analytics solutions. Proven track record of providing cutting-edge solutions that drive business adoption and value.
• Extensive project management experience and can anticipate and appropriately manage project milestones, risks, and impediments. Demonstrated history of appropriately communicating and escalating potential impediments and limitations to leadership.
• Demonstrated experience in guiding and mentoring junior technical staff in business interactions and model building.
• Extensive experience explaining and influencing complex technology decisions to both technical and nontechnical audiences at all levels in the organization and with cross functional and enterprise teams.
• Demonstrated experience leading business or product/portfolio transformation through use of advanced analytics.

Responsibilities

• Identifies, researches, and defines large-scale, cross-functional AI/ML use cases in collaboration with business leaders, executive peers, and strategic partners. Guides end-to-end efforts to develop scalable, efficient, highly-performant, automated AI/ML solutions.
• Applies deep expertise to amplify the impact of modeling techniques on emerging business initiatives. Collaborates with IT architects to design, implement, monitor, and scale cutting-edge AI/ML solutions that translate prototypes into novel products, services, and features.
• Ensures that AI/ML solutions are built using industry best practices, and sound methodology. Works with model risk partners to promote a culture of regulatory compliance.
• Designs large, complex information assets that enable applied analytics. Collaborates with engineering, data, and information architects to establish and maintain well-governed, documented, and controlled datasets from internal and external, structured, and unstructured sources.
• Seeks opportunities to simplify, modernize, and standardize the model development lifecycle. Provides expert technical advice and guidance by vetting vendor acquisitions.
• Actively raises the bar on talent and recruitment by leading or participating in communities of practice, talent development initiatives, and technical interview panels.
• Clearly translates complex analytical and technical concepts to diverse, technical, and non-technical audiences in a way that promotes organizational data literacy and informs business priorities.
• Provides technical oversight for building and maintaining a robust library of reusable, production-quality algorithms and supporting code.
• Develops and maintains academic and industry relationships for research purposes. Represents USAA in key internal/external technology and advanced analytics conferences.
• Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

Preferred Qualifications

• Deep technical expertise in machine learning and NLP and generative AI with hands on experience developing agent-based AI solutions tailored for banking use case.
• Extensive experience in a lead capacity in technology research, strategy, and implementation in the areas of Generative AI development for large scale cross-enterprise initiatives.
• Skilled at bridging business needs and technical solutions driving innovation in customer experience, contact center, underwriting and operational efficiency through advanced AI applications.