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Senior Quantitative Analytics Associate

JPMorgan Chase

Senior Quantitative Analytics Associate

JPMorgan Chase

Columbus, OH, United States, US

·

On-site

·

Full-time

·

2w ago

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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About JPMorgan Chase

JPMorgan Chase

JPMorgan Chase is a multinational investment bank and financial services company that provides banking, investment, and asset management services globally. It is one of the largest banks in the United States by assets and market capitalization.

300,000+

Employees

New York City

Headquarters

Reviews

4.2

10 reviews

Work Life Balance

4.2

Compensation

4.3

Culture

4.5

Career

4.4

Management

4.1

75%

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Pros

Good pay and benefits

Work-life balance

Career advancement opportunities

Cons

Heavy workload at times

Career advancement takes time

Pay could be better in some roles

Salary Ranges

47 data points

Junior/L3

Mid/L4

Senior/L5

Junior/L3 · Analyst

21 reports

$126,500

total / year

Base

$110,000

Stock

-

Bonus

-

$95,450

$155,250

Interview Experience

4 interviews

Difficulty

2.8

/ 5

Duration

14-28 weeks

Interview Process

1

Application Review

2

HireVue Video Interview

3

Technical/Behavioral Assessment

4

Final Interview Round

5

Offer Decision

Common Questions

Behavioral/STAR

Technical Knowledge

Past Experience

Culture Fit

Case Study