Data Scientist Senior - Model Development

USAA•San Antonio, NC

1d•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 provide 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 other data scientists to improve USAA’s tools and internal packages, and work with model risk management to validate model results and stability before production deployment. This 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.

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 experience may substitute for the degree.
Six years of experience in predictive analytics or data analysis; 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 one or more dynamic scripting languages, such as Python or R, for statistical analysis and/or building and scoring AI/ML models.
Proven ability to write clear, well-documented, transparent code, with comments as needed to explain logic.
Strong experience querying and preprocessing data from structured and/or unstructured databases using languages such as SQL, HQL, or NoSQL.
Strong experience working with structured, semi-structured, and unstructured data files, including delimited numeric files, JSON/XML files, text documents, or images.
Demonstrated skill in ad hoc analytics using descriptive, diagnostic, and inferential statistics.
Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
Advanced experience with classical supervised modeling for prediction, such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, and forest models.
Advanced experience with unsupervised modeling, such as 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 business recommendations and actionable applications.

Nice To Haves

Experience in 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 solutions using machine learning, simulation, and optimization to deliver business insights and value.
Select appropriate 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 technical documents for knowledge persistence, risk management, and technical review, and assist peers in doing so.
Assess business needs and recommend analytical and modeling projects that add business value.
Work with business and analytics leaders to prioritize analytics and modeling problems and research efforts.
Build and maintain reusable, production-quality algorithms and supporting code, ensuring model development and research are transparent and based on high-quality data.
Translate business requests into analytical questions, conduct analysis or modeling, and communicate outcomes and actionable recommendations to non-technical colleagues.
Manage project milestones, risks, and impediments, and escalate issues that could limit project success or implementation.
Develop best practices for working with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and risk management standards.
Maintain awareness of cutting-edge techniques and actively learn new techniques, 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.