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Quantitative Researcher — Exotic Equity Options | Front Office | Global Macro Fund | London - JMD Reg Consultancy LTD

JMD Reg Consultancy LTDLondon🇬🇧United KingdomPosted 24 Jul 2026

Quick Overview

Work Type
Hybrid
Schedule
Full Time
Level
Mid Senior

Job Description

A global macro fund is building out a new structured and exotic equity derivatives capability. This is a rare front-office quant role with a genuine path to becoming a trader and risk taker over time.

The hire will be the foundational quant, responsible for building pricing models and infrastructure from the ground up before transitioning into an enhance-and-maintain phase.

The successful candidate will work closely and directly with the PM, with growing responsibility for risk and P&L as the desk matures.

The Role
  1. Build and own exotic equity options pricing models from scratch in a greenfield environment
  2. Develop and implement models for barrier options , path-dependent payoffs , and broader exotic equity derivatives (autocallables, lookbacks, Asian options, cliquets)
  3. Build and calibrate local volatility and stochastic volatility surfaces (Heston, SABR, local-stochastic vol)
  4. Construct vol surface infrastructure and manage model lifecycle from research through to production
  5. Work directly with the incoming PM on model design, risk frameworks, and trading strategy
  6. Transition over time into a trading and risk-taking capacity as the desk develops

Candidate Requirements

Experience & Background:

  1. Approximately 5 years of front-office quantitative research experience on a sell-side exotic or structured equity vol desk
  2. Must have live, production model-building experience, this is not a model risk, validation, or control function role
  3. Demonstrable track record of owning models end-to-end: research → build → production

Technical Skills:

  1. Strong hands-on experience with exotic equity derivatives, barrier options, path-dependent payoffs, and the broader exotic toolkit are essential
  2. Proficiency in local vol and/or stochastic vol modelling (Heston, SABR, LSV)
  3. Strong C++ for model implementation; Python a strong plus
  4. Experience building greenfield quant infrastructure, not just maintaining inherited frameworks

Skills

Derivatives
C++
Python

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