Data Scientist Lead

USAA•Tampa, FL

23h•$164,780 - $314,960•Hybrid

About The Position

We are seeking a Lead AI Individual Contributor Data Scientist to drive the strategic adoption and practical application of AI, particularly Generative AI, across USAA Federal Savings Bank (USAA FSB). You will identify and implement AI solutions – whether cutting-edge industry advancements, existing solutions from other USAA lines of business, or tools that connect and integrate various platforms and IT systems – to boost productivity, automate processes, reduce costs, and create new revenue streams within the financial services context. This includes actively scouting for and evaluating external AI tools and technologies through various channels, such as professional networks and ties to academia, AI events, conferences, academic research, and a keen awareness of technological advancements, to ensure we are leveraging the most innovative solutions available. These efforts will encompass exploring AI solutions that serve as intelligent work assistants, streamlining and optimizing routine tasks for our employees, ultimately enhancing the experience and value delivered to our USAA members, while also considering the visionary potential and ethical implications of these advancements. This role is remote eligible in the continental U.S. with occasional business travel. However, individuals residing within a 60-mile radius of a USAA office will be expected to work on-site four days per week. Relocation assistance is available for this position.

Requirements

Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree.
8 years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline and 6 years of experience in predictive analytics or data analysis.
6 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
4 years of experience in one or more dynamic scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models.
Expert ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
Strong experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
Excellent demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
Project management experience that demonstrates the ability to anticipate and appropriately manage project milestones, risks, and impediments.
Demonstrated history of appropriately communicating potential issues that could limit project success or implementation.
Expert level experience with 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.
Expert level experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
Demonstrated experience in guiding and mentoring junior technical staff in business interactions and model building.
Demonstrated ability to communicate ideas with team members and/or business leaders to convey and present very technical information to an audience that may have little or no understanding of technical concepts in data science.
A strong track record of communicating results, insights, and technical solutions to Senior Executive Management (or equivalent).
Extensive technical skills, consulting experience, and business savvy to interface with all levels and disciplines within the organization.

Nice To Haves

An advanced degree (Master's/PhD) in STEM, Computer Science, Statistics, Economics, or Finance is required.
An additional degree in Information Technology (IT) or a closely related field is also strongly preferred, or a dual degree/major in one of these areas alongside a quantitative discipline.
Demonstrated ability to translate complex data analysis into actionable business insights and strategic recommendations.
Extensive experience in AI/ML/Data Science, focused on product development, strategy, or modeling.
Experience from leading tech companies (e.g., Apple, Meta, Amazon AWS, Google, FICO) in AI, automation, or digital product teams.
Served as an AI solution designer, developing and implementing AI solutions across diverse platforms and within complex data infrastructures.
Demonstrated hands-on experience with systems integration and the implementation of complex AI solutions.
Experience in IT roles or IT-adjacent functions, demonstrating a strong understanding of technology systems and infrastructure, is a plus.
Previous exposure to or understanding of the financial services sector is highly preferred.
Proven success in applying AI (especially GenAI) to enhance productivity, automate processes, reduce costs, or generate revenue.

Responsibilities

Gathers, interprets, and manipulates complex structured and unstructured data to enable advanced analytical solutions for the business.
Leads and conducts advanced analytics leveraging machine learning, simulation, and optimization to deliver business insights and achieve business objectives.
Guides team on selecting the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
Composes and peer reviews technical documents for knowledge persistence, risk management, and technical review audiences.
Partners with business leaders from across the organization to proactively identify business needs and proposes/recommends analytical and modeling projects to generate business value.
Works with business and analytics leaders to prioritize analytics and highly complex modeling. problems/research efforts.
Leads efforts to build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
Assists team with translating business request(s) into specific analytical questions, executing analysis and/or modeling, and communicating outcomes to non-technical business colleagues with a focus on business action and recommendations.
Manages project portfolio milestones, risks, and impediments.
Anticipates potential issues that could limit project success or implementation and escalates as needed.
Establishes and maintains best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
Interacts with internal and external peers and management to maintain expertise and awareness of cutting-edge techniques.
Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
Serves as a mentor to data scientists in modeling, analytics, computer science, business acumen, and other interpersonal skills.
Participates in enterprise-level efforts to drive the maintenance and transformation of data science technologies and culture.
Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.