• Design, implement, and improve machine learning Systems. • Contribute to machine learning applications end to end, i.e. from research to prototype to production. • Work with product managers, designers, and customers to define vision and strategy for a given product.

• Design, build, and iterate on ML-based entity resolution systems that match, link, and deduplicate supplier records across disparate data sources to produce trusted golden records • Build, train, and refine NLP and ML models (e.g., XGBoost, search ranking models) for supplier matching, classification, and data enrichment, with a focus on improving accuracy and recall • Evaluate and integrate emerging approaches, including LLMs, into our entity resolution and data intelligence workflows • Own the full ML model lifecycle: feature engineering, training, evaluation, monitoring, feedback loops, and iterative tuning in partnership with data engineering and product teams • Translate model results into business impact and clearly communicate tradeoffs, performance metrics, and recommendations to non-technical stakeholders • Build and maintain data products end-to-end, operationalize them within production data pipelines, and ensure they deliver reliable, scalable results • Execute and influence a cohesive data strategy that aligns with company objectives and supports analytics, reporting, and downstream product use cases • Own complex data modeling initiatives, including dimensional and analytical models that support business intelligence and advanced analytics • Drive continuous improvement by optimizing data pipelines, query performance, reliability, observability, and cost efficiency • Partner with Infrastructure, Product, and Engineering teams to ensure data systems meet best practices, security standards, and business needs • Create and maintain comprehensive technical documentation, including architecture diagrams, data flow maps, runbooks, and operations procedures • Troubleshoot and resolve complex, cross-system data issues and incidents.

Title: Senior Data Scientist Location: United States Type: Full-Time Job Description: Job ID: R0117789 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 Translates business problems into applied statistical, machine learning, simulation, and optimization solutions to inform 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. 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. What 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, 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, and ensemble methods such as Random Forests, XGBoost, LightGBM, and CatBoost. - 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 development Compensation: The salary range for this position is: $143,320- $257,970. USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.). 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. Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors. The 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. 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. 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.

• Design, build, and deploy machine learning solutions — including data pipelines, model training workflows, and LLM-powered experiences • Improve the performance, reliability, and accuracy of large language models in production environments • Define and track meaningful metrics that help us better understand user behavior and product impact • Partner cross-functionally with Product, Engineering, and business stakeholders to identify opportunities where data and AI can drive innovation • Build and maintain large-scale datasets that support advanced analytics and machine learning applications • Develop tools, models, and experimentation frameworks that help accelerate product development and decision-making • Serve as a technical thought leader, helping shape our approach to data science and AI across the company • Mentor and support other data scientists, contributing to a collaborative and growth-oriented team culture