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Applied AI/ML Associate Senior - Causal ML

JPMorganChaseBengaluru, Karnataka, IndiaPosted 16 May 2026

This Applied AI/ML Senior Associate role at JPMorganChase focuses on solving complex business problems within global wealth management. The position requires leveraging advanced machine learning, NLP, and causal inference techniques to build predictive and decision-support systems. Candidates will work with large datasets using Python, Spark, and Hive to model cause-and-effect relationships. This is an IC role suited for an analytical expert looking to apply research-level methodology to real-world financial use cases.

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Experience

Experience not specified

Function

Research

Work mode

Onsite, India

Company

Tier 1

What you will work on

This Applied AI/ML Senior Associate role at JPMorganChase focuses on solving complex business problems within global wealth management. The position requires leveraging advanced machine learning, NLP, and causal inference techniques to build predictive and decision-support systems. Candidates will work with large datasets using Python, Spark, and Hive to model cause-and-effect relationships. This is an IC role suited for an analytical expert looking to apply research-level methodology to real-world financial use cases.

TAL's take

Quality 85/1005/5 clarityTier 1 company

Role at a premier global financial institution focusing on advanced AI/ML applications in wealth management.

The JD clearly defines the causal ML niche, required tech stack, and responsibilities within the wealth management domain.

Salaries at JPMorganChase

24.4 LPA average

Based on 9 Grapevine salary entries for JPMorganChase.

View all salaries

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2 - 4 years | L1

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18 LPA average

Range: 15 - 21 LPA

Other roles

6 - 8 years | AVP

34 LPA average

Range: 34 - 34 LPA

Other roles

14 - 16 years | 602

35 LPA average

Range: 35 - 35 LPA

Must haves

  • Strong quantitative training in statistics, data science, or related field
  • Strong understanding of causal inference fundamentals
  • Practical knowledge of techniques to control for confounding
  • Advanced degree in an analytical field
  • Strong experience in natural language processing (NLP)
  • Solid coding level in Python
  • Experience working on Spark, Hive, and SQL

Tools and skills

pythontensorflowkeraspytorchscikit-learnsparkhivesqlcausal inferencemachine learningnatural language processing

Nice to have: causal forests, double machine learning, uplift models, a/b testing, synthetic control methods.

About the company

Global tier-1 financial institution with significant investment in technology and AI research.

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