Quantitative Model Validation Intern – AI/Analytics

UBS; Wrocław; On-site; PLN 28,000 - 42,000; Full time

Job summary

UBS is seeking an intern for its Chief Model Risk Function (CMRO) in Poland. The role focuses on validating models, assessing methodology and risks, and preparing validation reports. You will collaborate with stakeholders and gain hands‑on experience in risk governance and analytics.

The paid internship lasts 3–6 months with a 30–40 hour week, offering a pathway to longer-term opportunities. Fluency in English and a quantitative background are essential.

Qualifications

• Strong mathematics background (Bachelor’s or Master’s degree in quantitative area).
• Knowledge of financial products and risk management concepts.
• Data analytics and programming skills (Python, Excel).
• Good communication skills in English (oral and written).
• Quick learner with flexibility to adapt and produce high-quality analysis.

Responsibilities

• Assess a model's conceptual soundness and methodology.
• Check appropriateness of assumptions, parameters, model calibrations, qualitative or expert adjustments.
• Review outcome, impact, or benchmark analyses and develop a benchmark model (as appropriate).
• Assess model risk, including model robustness analysis and identification of limitations.
• Document the analysis, findings and conclusions in a validation report.
• Interact with stakeholders (model developer/owner, governance committees, senior management).

Skills

Mathematics

Financial concepts

Python

Excel

English communication

Education

Bachelor/Master in quantitative field

Job description

UBS is seeking an intern for its Chief Model Risk Function (CMRO) in Poland. The role focuses on validating models, assessing methodology and risks, and preparing validation reports. You will collaborate with stakeholders and gain hands‑on experience in risk governance and analytics.

The paid internship lasts 3–6 months with a 30–40 hour week, offering a pathway to longer-term opportunities. Fluency in English and a quantitative background are essential.