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Balyasny Asset Management

Quantitative Researcher - Risk (Summer Internship)

Asset Management·Detected 71 days ago·10 open offers at Balyasny Asset Management

  • Summer
  • London, United Kingdom

About the job

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Quantitative Researcher - Risk (Summer Internship)

London Posted Today

At BAM, our Researchers collaborate across asset classes to develop quantitative tools and insights that enhance our investment process.

The Risk team partners with Portfolio Managers, Quantitative Researchers, and technology teams to evaluate portfolio exposures, improve risk frameworks, and support portfolio construction across the firm.

As a Quantitative Research Intern on the Risk team, you will participate in a hands-on 10-week program designed to deepen your research capabilities.

You will work alongside Senior Quantitative Researchers and Risk Managers to solve real-world problems related to portfolio risk, portfolio construction, and the investment process.

The program offers mentorship, meaningful project work, and the opportunity to build relationships with the broader intern cohort.

Responsibilities

  • Conduct research and deliver insights related to portfolio and firm risk exposures, portfolio construction, and the investment process.
  • Use Python for exploratory data analysis, modeling, visualization, performance evaluation, and report generation.
  • Improve and evaluate models and analytical frameworks used to assess risk, support portfolio construction, and analyze trading and investment decisions.
  • Work with large, complex datasets to identify patterns, assess model performance, and communicate actionable findings.
  • Partner with Risk Managers and Senior Researchers to build tools and analyses that improve risk monitoring and investment decision-making.

Qualifications

  • Master's student graduating between Winter 2027 and Spring/Summer 2028, pursuing a degree in Mathematics, Statistics, Computer Science, Financial Engineering, Econometrics, Operations Research, or a related quantitative field.
  • Programming proficiency in Python.
  • Strong knowledge of probability, statistics, and quantitative modeling.
  • Experience working with large, complex datasets and conducting independent research in a data-driven environment.
  • Familiarity with financial markets, portfolio management, factor exposures, portfolio construction, or risk analytics is a plus.
  • Outstanding analytical skills and attention to detail.
  • Ability to clearly communicate complex and technical subject matter.
  • Pragmatic, collaborative, and results-driven approach to solving real-world investment problems.
  • Ability to work effectively in an ambiguous environment and manage multiple priorities.

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