Data Scientist

Position Summary

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.

• 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.

Outcome

Primary KPI

Reporting Cadence

Target / Direction

Analytic work delivers usable, decision-grade insight to business stakeholders.

Stakeholder-Accepted Deliverables — percent of analytic deliverables accepted by the requesting stakeholder without material rework.

Quarterly

• 90%

Production models perform within validated tolerances.

Model Performance Within Tolerance — percent of monitored models operating within defined performance and drift thresholds.

Monthly

• 95%

Forecast accuracy supports reliable financial planning.

Forecast Variance — absolute variance between forecast and actual on tracked financial metrics.

Monthly

• [Target — confirm with CIO]

Models and analytics are fully…