Oferty
Credit Risk Quantitative Analyst
BI & Data

Credit Risk Quantitative Analyst

Rodzaj pracy
Pełny etat
Doświadczenie
Starszy specjalista/Senior
Forma zatrudnienia
Dowolna
Tryb pracy
Praca hybrydowa

Wymagane umiejętności

Analysis

knowledge of IFRS9 and CRR standards

Python

Opis stanowiska

About Our Client

Join the Group Risk Quantitative Team, where we develop advanced credit risk models and implement innovative solutions for one of the world’s largest banks. As part of the team, you will contribute to cutting-edge risk management tools while integrating ESG elements, such as climate risk and model ethics.

Our Client's community consists of over 800 quantitative experts, offering unique opportunities for growth and collaboration in a dynamic international environment.


Your Responsibilities

  • Credit Risk Modeling and Analysis: Develop statistical methodologies, estimate parameters (PD, LGD), and assess model stability and performance.
  • ESG Integration: Support the incorporation of ESG risks (e.g., climate transition risk) into the Group's risk management framework.
  • Coding: Design and test codes implementing methodologies within internal systems.
  • International Collaboration: Participate in working groups, assist with regulatory recommendations, and analyze the impact of regulatory changes.


The Offer

  • A strategic and diverse role contributing to global risk management framework.
  • Opportunities to collaborate with teams across various countries and business areas.
  • The chance to work within a growing, Warsaw-based team of experts while being part of a community of 800+ Quants.
  • Potential for career growth within risk teams of other Group entities.


Your Profile

  • A university degree in computer science, economics, mathematics, or related fields.
  • At least 4 years of experience in quantitative analysis within credit risk, with solid knowledge of IFRS9 and CRR standards.
  • Advanced English skills, allowing you to actively participate in meetings and create technical documentation.
  • Proficiency in Python, PySpark, and Data Management/Data Visualization tools (e.g., SQL, Tableau, Dataiku). Basic knowledge of SAS is an advantage.
  • Autonomy, adaptability, analytical thinking with proactivity and creativity in solving non-standard problems.

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