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职位JPMorgan Chase

Senior Quantitative Analytics Associate

JPMorgan Chase

Senior Quantitative Analytics Associate

JPMorgan Chase

Columbus, OH, United States, US

·

On-site

·

Full-time

·

2mo ago

必备技能

Python

SQL

Customer Service

Ready to transform data into impactful insights? Join us as a Senior Quantitative Analytics Associate and make your mark with our dynamic team! Dive into data analysis, support diverse Lines of Businesses Wealth Management and drive strategic decision while advancing your career. Don’t miss this chance to leverage your skills, collaborate multiple lines of businesses and acquire knowledge on a variety of financial products.

As a Senior Quantitative Analytics Associate- Remediation & Corrections in Regulatory Operations, you will be crucial in identifying, classifying, and resolving customer impacts stemming from business process or operational disruptions at JP Morgan Chase. You will be involved in addressing affected customers by recalculating and crediting finance charges, fees, and processing account adjustments to rectify account issues. To succeed, you must be highly motivated, analytical, detail-oriented, and an outstanding problem solver who takes pride in managing customer issues comprehensively and delivering exceptional service.

Job responsibilities

  • Collaborate with key stakeholders across the firm to understand case contexts, including issues, and translate high-level requirements into detailed analytic steps.

  • Query databases and manipulate data to identify correction populations, financials, and create execution files using account, customer, and transaction-level data.

  • Ensure accuracy in analytics steps by paying attention to detail and supporting the independent validation team with case requirements and code.

  • Use SAS macros or other tools to automate repetitive analytics steps across cases.

  • Develop skills to deliver best-in-class analytics in the treatment of customer issues.

Required qualifications, capabilities, and skills

  • Bachelor's degree in a quantitative discipline (Mathematics, Statistics, Physics, Engineering, Economics, Finance or related fields)

  • 3+ years’ of experience with SQL and at least one of the following analytical tools: SAS, Python, R.

  • Experience working with at least one line of business within Chase Consumer and Community Banking

  • Strong communication skills (both written and verbal)

  • Detailed and quality oriented

  • Proven ability and commitment to mentoring junior team members

Preferred qualifications, capabilities, and skills

  • Capability to leverage artificial intelligence and AI tools to enhance data analysis, uncover business trends, and provide actionable insights for strategic decision-making.

  • Master’s degree with 3+ years’ of code development working experience in SQL/SAS

  • Demonstrated ability to influence and partner collaboratively with business partners

  • Demonstrated advanced troubleshooting and problem-solving skills with a customer service focus

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关于JPMorgan Chase

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+

员工数

New York City

总部位置

$500B

企业估值

评价

3.8

10条评价

工作生活平衡

3.2

薪酬

4.1

企业文化

3.8

职业发展

3.0

管理层

2.5

65%

推荐给朋友

优点

Good benefits and compensation

Supportive and collaborative environment

Flexible work arrangements

缺点

Long hours and heavy workload

Management issues and lack of direction

High stress during peak times

薪资范围

41个数据点

Junior/L3

Mid/L4

Senior/L5

Junior/L3 · Analytics Solutions Associate

1份报告

$139,000

年薪总额

基本工资

$107,000

股票

-

奖金

-

$139,000

$139,000

面试经验

5次面试

难度

3.0

/ 5

时长

14-28周

录用率

40%

体验

正面 20%

中性 80%

负面 0%

面试流程

1

Application Review

2

HireVue Video Interview

3

Recruiter Screen

4

Superday/Panel Interview

5

Final Interview

6

Offer

常见问题

Behavioral/STAR

Technical Knowledge

Culture Fit

Past Experience

Case Study