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Machine Learning Scientist - Quant AI - Senior Associate - Machine Learning Center of Excellence

JPMorgan Chase

Machine Learning Scientist - Quant AI - Senior Associate - Machine Learning Center of Excellence

JPMorgan Chase

Palo Alto, CA, United States, US

·

On-site

·

Full-time

·

4w ago

The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.

  • As a Machine Learning Scientist
  • Quant AI - Senior Associate within the Chief Data & Analytics Office (CDAO), you will work in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. The candidate must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.

Job Responsibilities:

  • Research, develop, and productionize high-performance machine learning and quantitative models.
  • Collaborate with JPMorgan's lines of business to deliver impactful software solutions.
  • Design and implement scalable data processing pipelines to optimize business results.

Required Qualifications, Capabilities, and Skills:

  • PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering) with 2 years of industry experience, or MS with 5 years of experience.
  • Proficient in programming languages such as C/C++, Java, or Python.
  • 2 years of experience in software development and data structures.
  • Deep knowledge in Machine Learning, Deep Learning, and related fields.
  • Experience with major machine learning frameworks like Tensor Flow and Py Torch.
  • Skilled in ETL pipelines and real-time data processing.
  • Strong analytical and critical thinking skills.
  • Self-motivated, with excellent communication and teamwork abilities.

Preferred Qualifications, Capabilities, and Skills:

  • Experience with computational graphs and JIT compilation.
  • Knowledge of hardware accelerators, GPUs, and profiling tools.
  • Familiarity with cloud computing platforms (Google Cloud, AWS, Azure).
  • Experience in distributed system design and development.
  • Background in derivatives modeling or portfolio management.

FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.

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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%

Recommend to a Friend

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

Mid/L4

Senior/L5

Mid/L4 · Applied AI ML Associate

2 reports

$188,500

total / year

Base

$145,000

Stock

-

Bonus

-

$182,000

$195,000

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