Mid-Level Data Scientist

USAA•San Antonio, TX

17h•$114,080 - $218,030•Hybrid

About The Position

At USAA, the mission is to empower members to achieve financial security through competitive products, exceptional service, and trusted advice, aiming to be the #1 choice for the military community and their families. The company values honesty, integrity, loyalty, and service. As a Mid-Level Data Scientist, you will leverage strategic insights, AI, and automation to support insurance business partners in protecting and serving members. This role seeks a collaborative and adaptable technical contributor to innovate solutions and re-engineer processes for business growth and improved claims experiences. It offers the opportunity to use data for strategic insight, solve complex challenges, and deliver significant value, especially for those passionate about making a real-world difference for members. The position involves translating business problems into applied statistical, machine learning, simulation, and optimization solutions to drive business value through automation, revenue generation, and risk reduction. You will collaborate with engineering partners to deliver scalable solutions and enable customer-facing applications, utilizing database, cloud, and programming knowledge to build analytical modeling solutions. Additionally, you will contribute to improving USAA's internal tooling and work with model risk management to validate models before production deployment. The role offers a flexible work environment requiring 4 days per week in the office, based in San Antonio, TX, Plano, TX, or Phoenix, AZ. Relocation assistance is not provided.

Requirements

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.
4 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 2 years of experience in predictive analytics or data analysis.
2 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
2 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.
Experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
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.
Experience in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Ability to assess regulatory implications and expectations of distinct modeling efforts.
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.
Experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors’ algorithms, DBSCAN, etc.
Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.

Nice To Haves

Business Impact-Driven Analytics: Extensive experience partnering with business subject matter experts to translate complex business problems into advanced analytical solutions. This involves critically evaluating approaches, translating technical findings into clear, business-oriented recommendations, and demonstrating a passion for driving the adoption of data-driven strategies that improve processes and create new, automated workflows.
End-to-End AI/ML Solution Delivery in Regulated Environments: Proven track record of collaborating with data scientists, data engineers, product manager, and IT to deliver AI/ML solutions to business partners. This includes a foundational understanding of Model Risk Management (MRM) and governance principles, essential for operating in highly regulated environments.
Software Engineering Rigor in Modeling: Demonstrated proficiency in software engineering best practices for data science, such as robust coding standards, package development and scripting, version control, and markdown-based documentation to allow for seamless collaboration with engineering teams for model deployment and productionization. We're looking for a strong understanding of bringing models from ideation through development and into production, utilizing non-notebook driven workflows for reproducibility. Experience in Python, Domino Data Labs, and Snowflake are preferred.
Expertise in Insurance and Customer Engagement: Experience in claims and/or marketing within property and casualty insurance, life insurance, health insurance, and/or annuities. This includes proven expertise in using analytics to drive marketing engagement and optimization, enhancing claims experiences, supporting call centers, and enhancing digital workflows and journeys.
Rigorous Model Validation and Experimentation: Experience in user acceptance testing, pilot testing, experimentation, and A/B testing to evaluate the value delivery of model solutions; experience with Bayesian analysis and/or event-driven simulation model development and their applications in a production environment.
US Military Experience: Military service or being a military spouse/domestic partner.

Responsibilities

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 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.
Participates in the prioritization of analytics and modeling problems/research efforts with business and analytics leaders.
Contributes to the development of 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 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.
Works closely with Data Engineering, IT, the business, and other internal stakeholders to deploy production-ready analytical assets that are aligned with the customer's vision and specifications while being consistent with modeling best practices and model risk management standards.
Maintains awareness of cutting-edge techniques.
Actively 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.