Associate – DB9330104
@ Deutsche Bank

New York, NY
$160,000
On Site
Full Time
Posted 1 day ago

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Job Details

Position Overview

Deutsche Bank Securities, Inc. is seeking an Associate – DB9330104 in New York, NY to implement a Retrieval-Augmented Generation (RAG) and Iterative Prompting pipeline integrated with a Large Language Model (LLM) and financial knowledge databases. The role focuses on automating the interpretation of trading-related inquiries and enhancing financial modeling frameworks.

Key Responsibilities

  • Implement RAG and Iterative Prompting pipeline with LLM integration.
  • Develop and enhance financial pricing models including SABR volatility models.
  • Utilize financial data portals (Bloomberg, SNL Financial) for data collection.
  • Build quantitative tools in Python for position analytics.
  • Maintain and optimize automated trading systems and C++ trading infrastructure.
  • Perform stress-testing and scenario analysis of interest rate options portfolios.

Qualifications

Applicants must hold a Master’s degree in Finance or a related field (or equivalent) and have at least one year of professional experience in using financial data portals and quantitative analysis techniques.

Culture & Inclusion

Deutsche Bank fosters a culture of responsibility, collaboration, and commercial insight. The firm celebrates individual and collective successes while promoting a positive, fair, and inclusive work environment for all employees.

Key skills/competency

  • RAG pipeline
  • Iterative Prompting
  • Large Language Model
  • Financial analysis
  • Bloomberg
  • SNL Financial
  • SABR volatility
  • Python
  • C++
  • Stress-testing

How to Get Hired at Deutsche Bank

🎯 Tips for Getting Hired

  • Customize your resume: Align experiences with quantitative finance roles.
  • Highlight technical skills: Emphasize Python, C++, and modeling expertise.
  • Research Deutsche Bank: Understand their culture and recent news.
  • Prepare for interviews: Focus on financial modeling and system optimization.

📝 Interview Preparation Advice

Technical Preparation

Review Python quantitative libraries and algorithms.
Practice C++ debugging and code optimization.
Study financial models and SABR volatility techniques.
Familiarize with Bloomberg and SNL Financial systems.

Behavioral Questions

Describe a time managing tight deadlines.
Explain how you handle team collaboration challenges.
Share an experience solving complex problems.
Discuss a situation adapting to market changes.

Frequently Asked Questions