Data Scientist Senior - Model Development

USAA•Tampa, FL

3d•$143,320 - $273,930•Remote

About The Position

As a dedicated Data Scientist Senior, you will translate business problems into applied statistical, machine learning, simulation, and optimization solutions to deliver 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 use database, cloud, and programming knowledge to build analytical modeling solutions, collaborate with data scientists to improve USAA’s tooling and internal packages, and work with model risk management to validate models before production deployment. The role is remote eligible in the continental U.S. with occasional business travel. Individuals residing within a 60-mile radius of a USAA office are expected to work on-site. USAA does not provide visa sponsorship for this role.

Requirements

Bachelor’s degree in a quantitative discipline such as mathematics, computer science, statistics, economics, finance, actuarial sciences, science, or engineering; alternatively, four additional years of relevant statistics, mathematics, or quantitative analytics experience may substitute for the degree.
Six years of predictive analytics or data analysis experience; alternatively, an advanced degree in a relevant quantitative discipline and four years of predictive analytics or data analysis experience.
Four years of experience training and validating statistical, physical, machine learning, or other advanced analytics models.
Four years of experience using a dynamic scripting language such as Python or R for statistical analysis or building and scoring AI/ML models.
Proven ability to write understandable, well-documented code, with comments where needed to explain logic.
Strong experience querying and preprocessing structured or unstructured database data using languages such as SQL, HQL, or NoSQL.
Strong experience working with structured, semi-structured, and unstructured files, including delimited numeric data, JSON/XML, text documents, and images.
Demonstrated ability to perform ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Ability to assess and explain regulatory implications and expectations for modeling efforts.
Advanced experience with classical supervised predictive modeling, including linear/logistic regression, discriminant analysis, support vector machines, decision trees, and forest models.
Advanced experience with unsupervised modeling, including k-means, hierarchical/agglomerative clustering, neighbors algorithms, and DBSCAN.
Experience guiding and mentoring junior technical staff in business interactions and model building.
Experience communicating analytical and modeling results to non-technical business partners, emphasizing recommendations and actionable applications.

Nice To Haves

Experience at a medium or large bank.
Strong knowledge of Python and/or SAS.
Experience collaborating with control partners.
Knowledge of Model Risk Management, Model Governance, and regulatory requirements.

Responsibilities

Gather, interpret, and manipulate structured and unstructured data for advanced analytical solutions.
Develop scalable, automated machine learning, simulation, and optimization solutions to deliver business insights and value.
Select modeling techniques and technologies based on data limitations, application, and business needs.
Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
Compose and assist peers in composing technical documents for knowledge persistence, risk management, and technical review audiences.
Assess business needs and recommend analytical and modeling projects; work with business and analytics leaders to prioritize problems and research efforts.
Build and maintain a robust library of reusable, production-quality algorithms and supporting code.
Translate complex business requests into analytical questions, conduct analysis or modeling, and communicate outcomes, actions, and recommendations to non-technical colleagues.
Manage project milestones, risks, and impediments, escalating issues that may limit success or implementation.
Develop best practices for working with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling and risk management standards.
Maintain expertise in emerging techniques and actively learn new technologies and methodologies.
Mentor junior data scientists in modeling, analytics, and computer science tasks.
Participate in internal communities that maintain and transform data science technologies and culture.
Ensure business risks are identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.