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求人JPMorgan Chase

Wholesale Credit Risk, Loan Loss Forecasting, IFRS 9

JPMorgan Chase

Wholesale Credit Risk, Loan Loss Forecasting, IFRS 9

JPMorgan Chase

Paris, France, FR

·

On-site

·

Full-time

·

1mo ago

必須スキル

Python

SQL

Excel

Tableau

The Legal Entity Loss Forecasting team provides solutions to legal entities, including capital and stress loss assessment to assist Internal Capital Assessment Adequacy Process (ICAAP), regulatory prescribed exercise, climate stress testing. The team recently expanded its scope to lead IFRS 9 production.

As an Associate within our Legal Entity Loss Forecasting team, you will participate in IFRS 9 and ICAAP production of numerous Legal Entities. You will be embedded in a global team and interact with various stakeholders. As an Associate, you will be responsible for understanding specific regulatory requirements and guaranteeing adequate application and consistency of methodologies.

Job responsibilities

  • Participate in the production of IFRS 9 for JPMC’s legal entities in EMEA, APAC and LATAM regions
  • Participate in the building/enhancement of loss forecasting frameworks
  • Leverage the firm’s infrastructure to perform quantitative analysis on the legal entities portfolio
  • Support the production of regulatory stress testing exercises, risk appetite and credit limit exercises as well as ad-hoc portfolio analytics asks
  • Collect data from various sources, synthetize the information, perform analysis, and interpret results in order to assist in making recommendations which impact legal entities
  • Prepare and present reports on risk analytics to technical and non-technical audiences as well as Senior Management audiences
  • Get familiar with the various regulatory requirements governing our legal entity strategy and determine how to optimize our approach to stress testing
  • Develop knowledge about the various stress testing platforms including ICAAP, IFRS9
  • Exploit your critical thinking and problem solving skills daily to drive results
  • Partner closely with Legal Entity Credit Risk, Finance, Quantitative Research and Technology, centrally and within legal entities, to assist in the understanding and enhancement of existing wholesale loan stress models

Required qualifications, capabilities and skills

  • Fluency in English is a must
  • Bachelor’s degree in Economics, Finance, Math/Statistics or other STEM majors
  • Good understanding of bank stress testing, traditional credit products and credit risk management
  • Proficient in analyzing large data sets using Tableau and Excel
  • Analytical mindset and strong problem-solving abilities, capability to learn new knowledges and apply to work in short time
  • First experience in IFRS 9 modelling
  • Excellent communication skills, verbal as well as written

Preferred qualifications, capabilities and skills

  • MBA/Master’s degree in STEM or finance, FRM or CFA
  • Experience in Python, SQL or R

総閲覧数

1

応募クリック数

0

模擬応募者数

0

スクラップ

0

JPMorgan Chaseについて

JPMorgan Chase

JPMorgan Chase & Co. is an American multinational banking institution headquartered in New York City and incorporated in Delaware. It is the largest bank in the United States, and the world's largest bank by market capitalization as of 2025.

300,000+

従業員数

New York City

本社所在地

$500B

企業価値

レビュー

3.8

10件のレビュー

ワークライフバランス

3.2

報酬

4.1

企業文化

3.8

キャリア

3.0

経営陣

2.5

65%

友人に勧める

良い点

Good benefits and compensation

Supportive and collaborative environment

Flexible work arrangements

改善点

Long hours and heavy workload

Management issues and lack of direction

High stress during peak times

給与レンジ

41件のデータ

Analyst

Junior/L3

Mid/L4

Senior/L5

Analyst · Analyst, Investment Banking

6件のレポート

$126,500

年収総額

基本給

$110,000

ストック

-

ボーナス

-

$103,500

$201,250

面接体験

5件の面接

難易度

3.0

/ 5

期間

14-28週間

内定率

40%

体験

ポジティブ 20%

普通 80%

ネガティブ 0%

面接プロセス

1

Application Review

2

HireVue Video Interview

3

Recruiter Screen

4

Superday/Panel Interview

5

Final Interview

6

Offer

よくある質問

Behavioral/STAR

Technical Knowledge

Culture Fit

Past Experience

Case Study