Description

Senior Quant Analyst / Research Scientist (Contract)

Location: London (cross-Atlantic collaboration)
Contract: 12 months (strong potential for extension/perm role)

Overview
A leading global asset management firm is seeking a senior buy-side Quant Analyst / Research Scientist to support its Fund-of-Funds portfolio within a Front Office-aligned Advanced Analytics function.
This is a high-impact contract mandate focused on:

• Hybrid annuity asset allocation modelling
• Cashflow forecasting
• Quadratic and convex optimisation
• Portfolio construction frameworks
• Python-based prototyping and deployment

The role sits at the intersection of quant research, front office portfolio management, and technology deployment, contributing directly to investment strategy and production-ready solutions.
You will work within a high-profile Applied R&D environment supporting Active Equities, Fixed Income, Risk Management, Corporate Finance and broader multi-asset strategies.

Core Mandate
The primary objective is to design and prototype quantitative models that support:

• Hybrid and annuity-style asset allocation
• Fund-of-funds portfolio construction
• Cashflow forecasting and liability-style modelling
• Constrained and quadratic optimisation problems

This is fundamentally a modelling-first mandate, requiring deep applied mathematics capability and hands-on implementation in Python.
AI/ML exposure is desirable but secondary - the role is not a pure AI engineering position.

Key Responsibilities

• Develop and implement quantitative models for hybrid annuity asset allocation
• Solve quadratic, convex, and mixed-integer optimisation problems
• Apply portfolio construction standards including:
  • Markowitz / Modern Portfolio Theory
  • Black-Litterman
  • Factor models
• Forecast portfolio cashflows and support annuity-style allocation structures
• Build robust prototype frameworks in Python
• Create comprehensive evaluation frameworks including:
  • Out-of-sample validation
  • Simulation
  • Back-testing
• Analyse model performance and robustness
• Collaborate directly with front office PMs on assumptions and outputs
• Engage with quant research and AI teams to industrialise modelling solutions
• Ensure models are production-ready and operationalised effectively within internal systems

Technical Environment

• Python (production-level proficiency required)
• Cloud-based research and development platforms:
  • SageMaker
  • Databricks
• Enterprise data infrastructure:
  • Snowflake
• Systematic research and quantitative workflows
• Investment management datasets across multi-asset strategies

Required Experience

• Senior quant experience within buy-side asset management or fund-of-funds environments
• Strong background in mathematical optimisation and applied modelling
• Portfolio construction / asset allocation expertise
• Hands-on Python development capability (not purely supervisory)
• Experience with equities; fixed income or hybrid portfolio exposure strongly preferred
• Experience creating model evaluation frameworks (OOS, simulation, back-testing)
• Experience working with investment management data
• Ability to read and computationally reproduce academic research
• Experience translating research outputs into production-grade solutions
• Comfortable collaborating across international teams (London / US)

Desirable Experience

• Exposure to ML / deep learning architectures
• Experience integrating AI/ML prototypes into production environments
• Multi-asset, insurance, or annuity product exposure
• Experience working alongside Front Office technology teams or PM management tools
• CFA participation or strong applied financial markets knowledge
• Graduate degree in a STEM discipline, or equivalent industrial research experience