Must have

• Data science

• Machine learning

• Python

• R

• SAS

• English

Requirements description

What you need to have to succeed in this role

• 4+ years of experience in model Development/Validation across diverse data science applications, including classification and regression models in credit risk, marketing, fraud detection.
• Knowledge of statistical models and machine learning techniques. Experience in assessing model performance.
• Good understanding of statistical modelling software / programming languages, e.g., Python, R, SAS.
• Master’s or PhD degree in a quantitative discipline like Financial Mathematics, Statistics, Econometrics, Quantitative Finance, Economics or Engineering.

Offer description

Some careers shine brighter than others.

If you’re looking for a career that will help you stand out, join HSBC, and fulfil your potential. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further.

ABOUT THE PROJECT

The role of the AVP is to perform model review (i.e., quantitative analysis and qualitative research with focus on model data, design, performance, and implementation) to formulate opinions about models’ conceptual soundness and adequacy for intended usage. Role responsibilities also cover documentation of conclusions and identified model risk indicators.

WHAT WE OFFER

• Competitive salary
• Annual performance-based bonus
• Additional bonuses for recognition awards
• Multisport card
• Private medical care
• Life insurance
• One-time reimbursement of home office set-up (up to 800 PLN).
• Corporate parties & events
• CSR initiatives
• Nursery discounts
• Financial support with trainings and education
• Social fund
• Flexible working hours
• Free parking

Your responsibilities

1. Comprehensive model reviews to assess conceptual soundness, data integrity, performance, implementation, and adherence to regulatory and/or internal requirements.
2. Being able to challenge constructively model owner choices.
3. Building effective relationships with the model developers to ensure proper communication and anticipate requests.
4. Support a high-performance culture through collaboration and effective people interaction.
5. Documentation of conducted assessments and model risk indicators in thorough review reports including recommendations for model improvements.
6. Tracking completion of review recommendations.