Position Overview
We are seeking a Data Scientist with strong AIOps experience to support enterprise-scale AI/ML systems within a highly regulated financial services environment. The role focuses on model validation, monitoring, governance, and operationalization of machine learning models.
The ideal candidate will bring both strong analytical depth and practical production-level ML experience, particularly in ensuring model performance, reliability, and regulatory compliance.
Key Responsibilities
- Develop, validate, and deploy machine learning models in production environments.
- Implement model validation frameworks to ensure statistical rigor and compliance.
- Design and maintain model monitoring systems (performance drift, data drift, bias detection).
- Support model governance processes including documentation, risk assessments, and audit readiness.
- Collaborate with engineering teams to operationalize models using AIOps/MLOps frameworks.
- Build automated pipelines for model retraining, testing, and performance tracking.
- Conduct root cause analysis for model degradation or performance issues.
- Ensure adherence to enterprise model risk management standards.
- Work closely with stakeholders to align analytical outputs with business objectives.
Required Qualifications
- 4–6 years of experience in Data Science or Machine Learning roles.
- Strong experience in AIOps or MLOps environments.
- Hands-on experience with model validation, monitoring, and governance.
- Proficiency in Python (NumPy, Pandas, Scikit-learn, etc.).
- Experience with ML lifecycle tools (MLflow, Kubeflow, SageMaker, or similar).
- Strong statistical knowledge and model evaluation expertise.
- Experience working in enterprise or regulated environments.
- Willingness to work 100% onsite in San Antonio, TX.