USAA-posted 9 months ago
$93,770 - $179,240/Yr
Full-time • Mid Level
Plano, TX
Credit Intermediation and Related Activities

As a dedicated Data Scientist - Intermediate Level, you will work with the Member and Master Data & Analytics Team in USAA's Enterprise Data & Analytics organization. They will work with Decision Science and Data Science analysts to build machine learning models, data visualizations, and advanced analytics solutions. They will also have an opportunity to contribute to Artificial Intelligence (AI) solutions, partner with Data teams to build analytical data in Snowflake, our target state Cloud platform. 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.

  • 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.
  • 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 may be substituted in lieu of degree.
  • 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 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.
  • Familiarity with performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
  • 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.
  • Ability to communicate analytical and modeling results to non-technical business partners.
  • Experience building machine learning models.
  • Experience with SQL, Python, Data Visualizations in Tableau or other BI tools.
  • Knowledge in Gen AI or AI is a plus.
  • Comprehensive medical, dental and vision plans.
  • 401(k) and pension.
  • Life insurance.
  • Parental benefits.
  • Adoption assistance.
  • Paid time off program with paid holidays plus 16 paid volunteer hours.
  • Various wellness programs.
  • Career path planning and continuing education.
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