Senior Quant Analyst — Risk & Model Innovation (Hybrid)

DW Search Ltd.; Greater London; Hybrid; GBP 104,000 - 127,000; Full time

Benefits offered by this job

Hybrid working

Competitive bonus

Job summary

DW Search Ltd. in London is seeking a Quantitative Analyst to join a small, highly technical team responsible for quantitative model validation globally across the firm. The role offers hybrid working and a base salary up to £115,000 with a strong bonus and benefits.

You will independently assess models, challenge underlying mathematics, and potentially contribute to development, enhancement and automation of quantitative risk models across investment teams and risk professionals.

Qualifications

• Strong mathematical and statistical foundations.
• Hands-on experience developing, analysing or implementing quantitative models.
• Strong programming skills, ideally Python.
• Broad intellectual curiosity and an interest in tackling very different quantitative problems.
• Communication skills to challenge model owners and investment professionals credibly.
• Strong quantitative academic background and/or substantial commercial quantitative experience.

Responsibilities

• Independently assess and challenge quantitative models used across the organisation.
• Rebuild a model independently and conduct quantitative analysis.
• Propose improvements and alternative approaches.
• Develop, enhance and implement quantitative risk models.
• Build analytical tooling and automation for the team.
• Explain complex quantitative conclusions to senior stakeholders.

Skills

Mathematical foundations

Statistical foundations

Quantitative modeling

Python programming

Communication skills

Tools

Python

Job description

DW Search Ltd. in London is seeking a Quantitative Analyst to join a small, highly technical team responsible for quantitative model validation globally across the firm. The role offers hybrid working and a base salary up to £115,000 with a strong bonus and benefits.

You will independently assess models, challenge underlying mathematics, and potentially contribute to development, enhancement and automation of quantitative risk models across investment teams and risk professionals.