Data Scientist

Position: Data Scientist – Model Governance, Validation & Monitoring

Location: San Antonio, TX (Onsite)

Job Summary

We are looking for an experienced Data Scientist with a strong background in model governance, model validation, model monitoring, and statistical modeling. The ideal candidate should have hands-on experience building, validating, deploying, and monitoring machine learning models throughout their lifecycle while ensuring compliance with governance standards and regulatory requirements.

Key Responsibilities

· Build, develop, and enhance statistical and machine learning models for business use cases.

· Perform independent model validation to assess model accuracy, robustness, and regulatory compliance.

· Monitor model performance and identify output/result drift over time.

· Evaluate data drift, concept drift, and model degradation, and recommend corrective actions.

· Classify and manage models based on risk levels (High Risk, Medium Risk, and Low Risk).

· Develop and execute model governance frameworks, standards, and documentation.

· Establish model monitoring processes, KPIs, and regular validation schedules.

· Manage end-to-end model lifecycle including development, validation, deployment, monitoring, and retirement.

· Deploy machine learning models into production and monitor post-deployment performance.

· Conduct periodic model reviews and revalidation to ensure continued effectiveness.

· Collaborate with cross-functional teams including Data Engineering, Risk, Business, and Technology teams.

· Prepare comprehensive model documentation, validation reports, and governance artifacts.

Required Skills

· Strong experience in Data Science, Machine Learning, and Statistical Modeling.

· Hands-on experience with Model Governance and Model Risk Management.

· Experience in Independent Model Validation (IMV).

· Strong understanding of Model Monitoring, Output Drift, Data Drift, and Concept Drift.

· Experience working with High, Medium, and Low Risk Models.

· Knowledge of Model Deployment and MLOps best practices.

· Proficiency in Python, SQL, and machine learning libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.

· Strong analytical, problem-solving, and statistical skills.

· Experience with model documentation and regulatory compliance is highly preferred.

Preferred Qualifications

· Experience in Banking, Financial Services, Insurance, or other highly regulated industries.

· Familiarity with model governance frameworks and audit requirements.

· Master''''s degree in Data Science, Statistics, Computer Science, Mathematics, or a related