Senior Data Scientist - Fraud Identity Analytics

Why USAA?Let's do something that really matters.At USAA, we have an important mission: facilitating the financial security of millions of U.S. military members and their families. Not all our employees served in our nation's military, but we all share in the mission to give back to those who did. We're working as one to build a great experience and make a real impact for our members.We believe in our core values of honesty, integrity, loyalty, and service. They're what guides everything we do - from how we treat our members to how we treat each other. Come be a part of what makes us so special!As a dedicated Senior Data Scientist, you will translate business problems into applied statistical, machine learning, simulation, and optimization solutions to advise actionable business insights and drive business value through automation, revenue generation, and expense and risk reduction. In collaboration with engineering partners, delivers solutions at scale, and enables customer-facing applications. Leverages database, cloud, and programming knowledge to build analytical modeling solutions using statistical and machine learning techniques. Collaborates with other data scientists to improve USAA's tooling, expanding the company's library of internal packages and applications. Works with model risk management to validate the results and stability of models before being pushed to production at scale.We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, Phoenix, AZ, Colorado Springs, CO, Charlotte, NC, Chesapeake, VA or Tampa, FL.Relocation assistance is not available for this position.This position can work remotely in the continental U.S. with occasional business travel.The OpportunityThe Senior Data Scientist - Fraud Identity Analytics is responsible for development and implementation of quantitative solutions that improve USAA's ability to detect and prevent identity theft, account takeover, and first party/synthetic fraud. These solutions will range from the development of machine learning models to broad implementation of solutions such as graph analytics to protect USAA and our Members from risks emanating from these threats. Strong candidates will be able to deploy the following work products and processes:• Develop and continuously update internal identity theft and authentication models to mitigate fraud losses and negative member experience from fraud application, synthetic fraud and account takeover attempts• Closely partner with Strategies team, Director of Fraud Identity Analytics and Director of Fraud Model Management and Model Users on model builds and priorities• Partner with Technology and other key collaborators to deploy a Financial Crimes graph database 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 and deliver highly significant benefits• 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• Collaborate with the broader analytics community to share standard methodologies and techniquesWhat you'll do:• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.• Develops scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.• Selects 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 assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences.• Assesses business needs to propose/recommend analytical and modeling projects to add business value. Works with business and analytics leaders to prioritize analytics and modeling problems/research efforts.• Builds and maintains 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.• Translates sophisticated business request(s) into specific analytical questions, completes the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations.• Manages project breakthroughs, risks, and impediments. Intensifies potential issues that could limit project success or implementation.• Develops 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.• Maintains expertise and awareness of innovative techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.• Serves as a mentor to junior data scientists in modeling, analytics, and computer science tasks.• Participates in internal communities that drive the maintenance and transformation of data science technologies and culture.• Ensures risks associated with business activities are optimally identified, measured, supervised, 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.• 6 years of experience in a predictive analytics or data analysis• 4 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.• Proven experience writing code that is easy to follow, well documented, and commented where vital 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.• Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.• Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.• Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.• Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.• Experience guiding and mentoring junior technical staff in business interactions and model building.• Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.What sets you apart:• US military experience through military service or a military spouse/domestic partner• Graduate degree in a quantitative subject area• Over 4 years of experience with model development or other advanced fraud detection algorithms• Over 3 years of experience with graph databases and graph solutions• Experience in fraud/financial crimes model developmentThe above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job.What we offer:Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location. The salary range for this position is: $138,230 - $248,810.Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors.Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals.For more details on our outstanding benefits, please visit our benefits page on USAAjobs.com.Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting.USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.