Lead Graph Data Scientist - Identity Analytics

Why USAA?

At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families.

Embrace a fulfilling career at USAA, where our core values – honesty, integrity, loyalty and service – define how we treat each other and our members. Be part of what truly makes us special and impactful.

We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs.

The Opportunity

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 not available for this position.

Job Description

The Lead Graph Data Scientist - Identity Analytics is responsible for development and implementing quantitative solutions that improve USAA's ability to detect and prevent identity theft, account takeover, and first-party/synthetic fraud. These solutions range from machine learning model development to enterprise deployment of graph analytics capabilities that protect USAA and our Members from these threats. Strong candidates will be able to deliver the following work products and processes:

Develop and continuously update internal identity theft and authentication models to mitigate fraud losses and reduce negative member experience from fraud applications, synthetic fraud, and account takeover attempts

Closely partner with the Strategy team, Director of Fraud Identity Analytics, Director of Fraud Model Management, and model users on model builds and priorities.

Partner with Technology and other key collaborators to deploy a Member Protection graph technology strategy, including vendor selection, business requirements, data needs, and clear use cases spanning financial crimes

Deploy graph databases and graph techniques to identify criminal networks engaging in fraud, scams, disputes/claims, and AML, improving fraud detection and loss mitigation

Generate and prioritize fraud-dense rings to mitigate losses and improve Member experience

Identify and work with technology to integrate new data sources for models and graphs to augment predictive power and improve business performance

Exports insights to decision systems to enable better fraud targeting and model development efforts

Drives continuous innovation in modeling efforts including advanced techniques like graph neural networks

Develops and mentors junior staff, establishing a culture of R&D to augment the day-to-day aspects of the job

What you'll do

Gathers, interprets, and manipulates sophisticated structured and unstructured data to enable sophisticated analytical solutions for the business.

Leads and conducts sophisticated analytics demonstrating machine learning, simulation, and optimization to deliver business insights and achieve business objectives.

Guides the team selecting the appropriate modeling technique and/or technology with consideration for 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 propose/recommend analytical and modeling projects to generate business value.

Works with business and analytics leaders to prioritize analytics and highly sophisticated modeling problems/research initiatives.

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 highest-quality data.

Assists the team with translating business request(s) into specific analytical questions, implementing 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 standard methodologies 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 leading 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.

What you have

Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field; 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 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 languages (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, 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, and ensemble methods such as Random Forests, XGBoost, LightGBM, and CatBoost.