Data Scientist

Credit Union of Texas•Allen, TX

4d•Hybrid

About The Position

The Data Scientist supports Credit Union of Texas's vision to be the trusted financial partner for our members and our community by applying statistical, machine learning, and data engineering techniques to credit union data. Working within the Finance function and partnering broadly across the organization, the role develops dashboards, reports, predictive models, and analytics that inform financial forecasting, member experience, marketing effectiveness, and operational decisioning. The Data Scientist collects and integrates operational and market data, identifies trends and variances against forecast, designs and validates models, and operationalizes analytics in partnership with business stakeholders. The role uses CUTX-approved AI and machine learning tools under defined governance, with mandatory human-in-the-loop review on any model output that influences member or financial outcomes.

Requirements

Master's degree in Engineering, Computer Science, Data Science, Statistics, Mathematics, Finance, or another quantitative field required.
Two (2) to five (5) years of experience manipulating data sets and building statistical and machine learning models.
Hands-on experience querying databases and using statistical computing languages such as R, Python, and SQL.
Experience applying statistical and data mining techniques including GLM/regression, random forest, boosting, decision trees, text mining, and social network analysis.
Experience designing and using machine learning algorithms (regression, simulation, scenario analysis, clustering, decision trees, neural networks).
Experience visualizing and presenting data and analytic findings to non-technical stakeholders.
Strong problem-solving skills with an emphasis on translating business questions into analytic solutions.
Proficiency in statistical computing languages (R, Python, SQL) for manipulating data and drawing insights from large data sets.
Knowledge of data architecture, data pipelines, and data quality practices.
Working knowledge of a variety of machine learning techniques (clustering, decision trees, random forest, boosting, neural networks, text mining) and a clear understanding of their real-world advantages and limitations.
Working knowledge of advanced statistical techniques (regression, distributions, hypothesis testing) and appropriate application.
Excellent written and verbal communication skills for coordinating across technical and business teams.
Drive to learn and adopt new technologies, techniques, and CUTX-approved AI tools.
Responsible for all enterprise compliance obligations applicable to a CUTX team member, including BSA/AML, OFAC, USA PATRIOT Act/CIP/CDD, GLBA and the Safeguards Rule, Fair Lending laws (ECOA/Reg B, Fair Housing Act), UDAAP, Information Security and Acceptable Use, and the CUTX Code of Conduct.
Responsible for the CUTX Generative AI Usage Policy (TRAIGA / HB 149-aligned), the CUTX AI Playbook (including Tier 2 obligations applicable to this role), and Texas Responsible Artificial Intelligence Governance Act (TRAIGA / HB 149) requirements applicable to the role.
CUTX Model Risk Management standards covering model development, validation, documentation, monitoring, and change control.
Fair Lending laws (ECOA/Reg B, Fair Housing Act) as applied to any model or analytic that could influence credit, pricing, or marketing decisions.
UDAAP standards as applied to member-facing analytics, targeting, and product design.
Gramm-Leach-Bliley Act (GLBA) and the Safeguards Rule governing the handling of member non-public personal information in analytic workflows.
Fair Credit Reporting Act (FCRA) considerations where analytics interact with consumer report data.
CUTX Data Governance, Data Classification, and Information Security policies as applied to analytic data sets and code repositories.

Nice To Haves

Knowledge of the financial services industry is preferred.
Prior credit union, banking, or financial services analytics experience preferred.
Prior experience with A/B testing frameworks and model monitoring in production preferred.
Familiarity with model risk management and AI governance concepts is preferred.

Responsibilities

Partner with stakeholders across the organization to identify opportunities to leverage CUTX data to drive business solutions and improve member outcomes.
Mine and analyze data from CUTX databases and external sources to drive optimization of product development, marketing techniques, and business strategies.
Develop custom data models, algorithms, and feature sets applied to credit union data, including loan, deposit, transaction, and member-behavior data.
Apply predictive modeling to support member experience, revenue generation, targeting, retention, and other business outcomes.
Apply statistical methods (regression, distribution analysis, hypothesis testing) and machine learning techniques (clustering, decision trees, random forest, boosting, neural networks, text mining) appropriate to the business question.
Develop and maintain financial forecasts, dashboards, and reports that compare actual results against forecast and identify material trends and variances.
Collect and integrate operational and market data to support financial analytics for the CIO and Finance leadership.
Translate analytical findings into clear written and verbal narratives suitable for executive and cross-functional audiences.
Work with and contribute to data architectures that support repeatable, auditable analytics.
Assess the effectiveness and accuracy of new data sources and data-gathering techniques before they are adopted for production use.
Build and maintain data pipelines, queries, and reusable analytic assets using SQL, Python, R, or other CUTX-approved tools.
Develop and operate an A/B testing framework and test model quality before and after deployment.
Coordinate with functional teams to implement models in production and monitor outcomes against expected performance.
Develop processes and tools to monitor model performance, data quality, drift, and accuracy over time, and document validation results.
Document model purpose, data inputs, assumptions, limitations, validation results, and intended use consistent with CUTX model risk and AI governance expectations.
Coordinate across Finance, Marketing, Lending, Risk, IT, and Compliance to align analytic work with enterprise priorities.
Continuously develop technical and domain skills, including credit union and financial services knowledge, to improve the relevance and quality of analytic work.
Apply human-in-the-loop review on every model output, recommendation, or analytic finding that informs a member-impacting or material financial decision before it is acted upon.
Validate AI-assisted code, queries, and analytic artifacts before they are used in production or shared with stakeholders.
Escalate to the CIO and the AI Council any use case that involves direct member-impacting decisioning, automated adverse action, or regulated decisioning, which is treated as Tier 3 and requires additional controls.
Stop reliance on AI or model output and escalate immediately if the output appears inaccurate, biased, non-compliant, or outside the role's documented scope (Generative AI Usage Policy §3.5).
Refrain from entering member non-public personal information (NPI), confidential CUTX information, or material non-public information into any AI tool not explicitly approved for that data classification.
Complete all required AI training within thirty (30) days of hire and maintain annual currency.