Data Scientist with Model Governance, Validation & Monitoring

Hi All 

This is Lokesh. I hope you are doing great

I have an urgent requirement of Data Scientist with my client located in TX 

I am attaching the JD below and let me know if you have any questions 

Job Title: Data Scientist – Model Governance, Validation & Monitoring

Location: San Antonio, TX (Onsite)

Experince: 12+ Years

W2 role 

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 field.