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AI ML Executive Director

JPMorgan Chase

AI ML Executive Director

JPMorgan Chase

New York, NY, United States, US

·

On-site

·

Full-time

·

5mo ago

As an AI/ML Executive Director, in our AI for Operations organization, you will work on developing cutting-edge large scale Agentic-AI, GenAI, Natural Language Processing and Search Systems to serve our customers and internal agents supporting the bank’s operations.

Job Responsibilities

  • Utilize large-scale distributed computing to effectively apply Agentic-AI in real-world applications to serve JPMC customers and service agents across channels.

  • Use and extend GenAI capabilities to build differentiating applications that are customized to suit product and customer requirements. Effectively and efficiently develop various evaluation mechanisms to identify, address and improve the results of LLM based systems.

  • Apply deep natural language processing (NLP) knowledge & experience and critical thinking skills and perform advanced analytics with the goal of solving complex and multi-faceted business problems.

  • Build and Lead a team of Machine Learning applied researchers and engineers. Provide technical and career guidance to the team members.

  • Collaborate with cross-functional partners including product, data and engineering.

  • Lead by example to provide the best service and build the most appropriate applications in a timely fashion to address customer concerns, resolving critical issues, and push forward on future roadmap and priorities.

  • Contribute to the full product development lifecycle, including defining the objective and key product deliverables.

  • Act as an advanced architect and code contributor in system development, algorithms, architectural design for Agentic-AI, GenAI, NLP and machine learning systems.

  • Contribute to the continuous learning mindset of the organization by bringing in new knowledge, ideas, and perspectives.

Required qualifications, capabilities, and skills:

  • PhD in Computer Science, with a strong research and industry work experience in AI/ML, Natural Language Processing and Information Retrieval.

  • Outstanding written and oral communication skills to present analytical findings and exercise influence among key project stakeholders.

  • Hands-on extensive experience in developing large-scale machine learning solutions based on big data to solve real world problems (e.g. Classification, Regression, or Recommender Systems).

  • Advanced demonstrable programming skills of 10-15 years (PhD plus industry experience) on more than 1 programming language is required. Preferred: Spark, Python, Scala, Java.

  • Strong background in data structures, algorithms, operating systems, compilers, distributed computing and databases.

Preferred qualifications, capabilities, and skills:

  • PhD in computer science with concentration in AI - NLP or Information Retrieval.

  • Full understanding and advanced programming skills using distributed infrastructure, platforms, and computational methods (including distributed ML).

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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