- Company Name
- Sagen
- Job Title
- Data Scientist, Model Risk
- Job Description
-
**Job Title**
Data Scientist, Model Risk
**Role Summary**
Provide independent oversight of predictive and analytical models across the organization. Conduct rigorous technical validation, assess model soundness, performance, and governance, and collaborate with model owners to ensure risk mitigation and transparency.
**Expectations**
- Deliver unbiased model assessments in alignment with regulatory and internal standards.
- Produce clear, business‑focused insights and actionable recommendations for model developers.
- Maintain robust documentation, reporting, and presentation of validation findings to governance committees and senior leadership.
**Key Responsibilities**
- Coordinate with Finance, Risk, Operations, and IT stakeholders to execute independent model validation cycles.
- Evaluate conceptual soundness, data quality, feature engineering, methodology, and performance of predictive and analytical models.
- Perform technical reviews of ETL processes, data pipelines, model code, and deployment workflows, ensuring reproducibility and accuracy.
- Assess model assumptions, limitations, monitoring plans, and controls; recommend remediation actions.
- Prepare validation reports and support senior leadership presentations.
- Develop challenger models to benchmark performance and challenge existing models.
- Support model lifecycle activities (documentation, testing, monitoring, change management) in line with internal standards and industry best practices.
- Identify and implement automation and efficiency improvements in validation processes and risk analytics.
**Required Skills**
- Advanced programming: Python, R, SQL, PySpark.
- Proficient with ML/Bayesian/DL frameworks (scikit‑learn, MLlib, Keras, PyTorch).
- Experience with AWS cloud services, Docker containerization, GitHub Actions, and modern CI/CD pipelines.
- Familiarity with Alteryx, Power BI, Tableau, and data visualization tools.
- Comfortable with Git workflows, Jira, Confluence, Unix environments, and microservices architecture.
- Strong analytical, proactive, and collaborative mindset.
- Excellent communication skills—capable of translating technical findings into clear, business‑oriented insights.
**Required Education & Certifications**
- Advanced degree (Master’s or Ph.D.) in Statistics, Mathematics, Computer Science, Econometrics, Engineering, or a related quantitative field.
- Minimum 1–2 years’ experience in machine learning, Bayesian, deep learning, or AI model development with demonstrated experience in model validation or model risk.