Data Scientist Senior – Workforce Planning/Forecasting

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.

The Opportunity

This position can work remotely in the continental U.S. with occasional business travel.

The selected candidate will be responsible for Work Force Optimization capabilities and projected forecasts, crafting data-driven hiring plans using dynamic modeling techniques.

Turn 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.

What you’ll do:

• Captures, 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 complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations.

• Manages project milestones, risks, and impediments. Escalates potential issues that could limit project success or implementation.

• Develops 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.

• Maintains expertise and awareness of cutting-edge 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 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 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.

• 6 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 4 years of experience in 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