Data Scientist II (Mid Level) - Pricing and Product Analytics at USAA in San Antonio Home Office I

Job description

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 We are seeking a dedicated Data Scientist II (Mid Level) – Pricing and Product Analytics. We have multiple remote-eligible opportunities available on our Auto and Property Data Science teams that build loss, acquisition, retention, and lifetime value models. 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. What you'll do: As a Data Scientist, you will translate 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, you will deliver solutions at scale, and enable customer-facing applications. You will leverage database, cloud, and programming knowledge to build analytical modeling solutions using statistical and machine learning techniques. You will collaborate with other data scientists to improve USAA's tooling, expanding the company's library of internal packages and applications and work with model risk management to validate the results and stability of models before being pushed to production at scale. Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business. 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 technical documents for knowledge persistence, risk management, and technical review audiences. Consults with peers for guidance, as needed. Translates business request(s) into specific analytical questions, executing on the analysis and/or modeling, and communicating outcomes to non-technical business colleagues. Consults with Data Engineering, IT, the business, and other internal stakeholders to deploy analytical solutions that are aligned with the customer's vision and specifications and consistent with modeling best practices and model risk management standards. Seeks opportunities and materials to learn new techniques, technologies, and methodologies. 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 Science, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience. 2 years of experience in 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. Experience in training and validating statistical, physical, machine learning, and other advanced analytics models. 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. Ability to write code that is easy to follow, well documented, and commented w