Quant Lab Trading Internship | ML/NLP & Quant Research

Quant Lab Trading Internship | ML/NLP & Quant Research

Internship 22500 - 27500 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Apply ML and NLP techniques to real business problems in a hands-on internship.
  • Company: Join Deutsche Bank AG, a leader in the financial sector with a collaborative culture.
  • Benefits: Gain mentorship, practical experience, and exposure to a fast-paced environment.
  • Other info: 18-week internship starting May–June 2027, perfect for aspiring quant researchers.
  • Why this job: Make an impact in quantitative research while developing your skills in a dynamic setting.
  • Qualifications: Postgraduate degree in a quantitative field with strong academic performance.

The predicted salary is between 22500 - 27500 £ per year.

Deutsche Bank AG invites applications for its 2027 UK Quant Internship programme.

You will work in QRD Labs, applying ML and NLP techniques to real business problems.

Expect hands-on projects, mentorship, and exposure to a fast-paced, collaborative culture within Deutsche Bank.

Applicants should be pursuing a postgraduate degree in a quantitative field, with outstanding academic records.

The internship runs for 18 weeks starting May–June 2027, with a focus on building practical research

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Quant Lab Trading Internship | ML/NLP & Quant Research employer: Deutsche Bank AG

Deutsche Bank AG is an exceptional employer, offering a dynamic work environment in the heart of London where innovation meets collaboration. As a Market Risk Manager, you will benefit from a culture that prioritises professional growth and development, alongside competitive compensation and comprehensive benefits. The opportunity to engage with diverse teams and contribute to the expansion of the global Energy business makes this role both meaningful and rewarding.

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Contact Details:

Deutsche Bank AG Recruitment Team

We think you need these skills to ace Quant Lab Trading Internship | ML/NLP & Quant Research

Machine Learning (ML)
Natural Language Processing (NLP)
Quantitative Analysis
Research Skills
Data Analysis
Statistical Modelling
Programming Skills