Data Scientist I

USAA•Tampa, FL

1d•$114,080 - $218,030•Hybrid

About The Position

As a Data Scientist, you will translate complex business challenges into practical, data-driven solutions using statistical analysis, machine learning, simulation, and optimization techniques. Your work will drive meaningful business impact by enabling automation, uncovering revenue opportunities, and reducing risk and operational costs. In partnership with engineering teams, you will design and deliver scalable solutions that support internal decision-making and power customer-facing applications. You will leverage your expertise in databases, cloud platforms, and programming to develop advanced analytical models, while collaborating with peers to enhance internal tools and expand the organization’s library of data science capabilities. Success in this role requires a strong technical foundation and domain expertise within banking and financial services, including an understanding of business processes and risk management practices. You will work across modern data and cloud ecosystems, utilizing tools such as AWS (Redshift, Glue, S3, Lambda), PySpark, and ETL technologies like DataStage and Informatica. Proficiency in Python visualization libraries (Matplotlib, Seaborn, iPython), Git for CI/CD automation, and workflow orchestration tools like Apache Airflow and BMC Control-M is essential. Additionally, experience with data engineering, cloud architecture, and Agile methodologies will support your ability to build, deploy, and maintain reliable models, working closely with model risk management to ensure accuracy and stability before production deployment. We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, Phoenix, AZ, Colorado Springs, CO, Charlotte, NC, or Tampa, FL. Relocation assistance is not available for this position.

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

US military experience through military service or a military spouse/domestic partner
Strong technical foundation with domain expertise in banking and financial services, including knowledge of business processes and risk management practices
Experience working across modern data and cloud ecosystems, including AWS services (Redshift, Glue, S3, Lambda) and PySpark
Hands-on experience with ETL tools such as DataStage and Informatica
Proficiency in Python visualization libraries (Matplotlib, Seaborn, iPython)
Experience with Git and CI/CD automation practices
Familiarity with workflow orchestration tools like Apache Airflow and BMC Control-M
Knowledge of data engineering principles and cloud architecture
Experience working in Agile environments and methodologies
Ability to build, deploy, and maintain reliable models, partnering with model risk management to ensure accuracy and stability before production deployment

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.