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职位JPMorgan Chase

Data Scientist Associate - Bengaluru

JPMorgan Chase

Data Scientist Associate - Bengaluru

JPMorgan Chase

Bengaluru, Karnataka, India, IN

·

On-site

·

Full-time

·

2w ago

J.P. Morgan is a leading global financial services firm, established over 200 years ago.

JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2.5 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com.

We have an exciting opportunity for you to advance your data science career and shape the future of AI-driven solutions.

As a Data Scientist Associate within the Home Lending Originations Data and Analytics Team, you will collaborate with cross-functional teams to construct predictive models and develop robust RAG pipelines. You will extract valuable insights from complex datasets, drive data-driven decision-making, and deliver innovative AI solutions that enhance technology and business efficiency.

Responsibilities:

  • Design, develop, and deploy classical ML models (Classification & Regression) and GenAI solutions (LLMs, RAG, prompt engineering), and Agentic AI workflows
  • Design, develop, and manage prompt-based models on Large Language Models for financial services tasks
  • Architect and oversee the development of next-generation machine learning models and systems using advanced technologies
  • Align machine learning problem definition with business objectives to address real-world needs
  • Drive innovation in machine learning solutions with a focus on scalability and flexibility
  • Promote software and model quality, integrity, and security across the organization
  • Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications for enterprise deployment
  • Integrate GenAI solutions with enterprise platforms using API-based methods

Role Description:

Data Scientist required for Home Lending Data & Analytics, supporting key strategic business priorities across HL Origination & Servicing

  • 4+ years of AI/ML experience in Classical ML, Generative AI, and Agentic AI
  • Hands-on experience building best-in-class supervised & unsupervised ML models
  • Strong knowledge of ML algorithms – Logistic Regression, XGBoost, Random Forest, Gradient Boosting, K-Means, PCA, Anomaly Detection, etc.
  • Experience with LLMs, prompt engineering, fine-tuning, RAG, and orchestration frameworks (Lang Chain, Llama Index)
  • Familiarity with Agentic AI – multi-agent orchestration, tool-use agents, and frameworks like Lang Graph, CrewAI, Auto Gen
  • Strong Python & SQL skills with proficiency in ML/AI libraries (scikit-learn, Py Torch/Tensor Flow, Hugging Face)
  • Experience with MLOps (MLflow, Kubeflow) and cloud platforms (AWS/Azure/GCP) for model deployment

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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

Mid/L4

Senior/L5

Mid/L4 · Applied AI ML Associate

2份报告

$188,500

年薪总额

基本工资

$145,000

股票

-

奖金

-

$182,000

$195,000

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