Quantitative Analyst / Quantitative Programmer, Global Asset Manager

Logansinclair; Greater London; Hybrid; GBP 70,000 - 120,000; Full time

Job summary

Our client is a global asset manager seeking a Quantitative Analyst to advance asset‑simulation models underpinning Strategic Asset Allocation, ALM and lifecycle investing. The role blends research, model engineering and client‑facing work with production‑grade Python/C++ code.

The ideal candidate has 3–5 years in asset management or investment banking, strong quantitative foundations, and experience deploying models to production. The position is London‑based and highly technical.

Qualifications

• Master’s in Mathematics, Statistics, Computer Science, Economics or Financial Engineering.
• 3–5 years as a quantitative analyst/programmer in asset management or investment banking.
• Strong grounding in probability theory, stochastic calculus and statistical inference.
• Experience across liquid and illiquid assets, asset allocation and portfolio optimisation.
• Practical exposure to bond pricing, stochastic volatility modelling and Monte Carlo simulations.
• Proficient in time‑series analysis, econometrics and factor‑based modelling.
• Advanced Python (numpy, pandas) with production deployment experience.
• C++ highly valued; SQL proficiency; MS Office with VBA a plus.
• Clear communicator able to explain complex ideas to non‑specialists.

Responsibilities

• Build, calibrate and maintain a proprietary asset‑simulation platform.
• Specify capital market assumptions and generate asset‑class simulations.
• Design macro‑financial models in Python and/or C++.
• Tailor internal models to varied optimisation and simulation use‑cases.
• Create ad‑hoc tools in Python and Excel for bespoke analyses.
• Support Strategic Asset Allocation, ALM and lifecycle investing (incl. decumulation).
• Inform forecasting and portfolio construction across markets and asset classes.
• Deliver technical support to sales/clients and present methods and results clearly.
• Produce clean, tested code; use Git and deploy to production.
• Automate and scale quantitative research workflows.

Skills

Python

C++

SQL

Communicator

Time-series

Education

Master’s in Mathematics/Statistics/CS/Economics/Financial Engineering

Tools

Python (numpy, pandas)

C++

SQL

Excel/VBA

Job description

Quantitative Analyst / Quantitative Programmer, Global Asset Manager

Location London

Compensation Competitive

Description

Our client is a global asset manager with a specialist institutional and retirement solutions focus. They seek a Quantitative Analyst to advance asset-simulation models underpinning Strategic Asset Allocation, ALM and lifecycle investing (including decumulation). The role blends research, model engineering and selective client-facing work.

Responsibilities

• Build, calibrate and maintain a proprietary asset‑simulation platform.
• Specify capital market assumptions and generate asset‑class simulations.
• Design macro‑financial models in Python and/or C++.
• Tailor internal models to varied optimisation and simulation use‑cases.
• Create ad‑hoc tools in Python and Excel for bespoke analyses.
• Support Strategic Asset Allocation, ALM and lifecycle investing (incl. decumulation).
• Inform forecasting and portfolio construction across markets and asset classes.
• Deliver technical support to sales/clients and present methods and results clearly.
• Produce clean, tested code; use Git and deploy to production.
• Automate and scale quantitative research workflows.

Requirements

• Master’s in Mathematics, Statistics, Computer Science, Economics or Financial Engineering.
• 3–5 years as a quantitative analyst/programmer in asset management or investment banking.
• Strong grounding in probability theory, stochastic calculus and statistical inference.
• Experience across liquid and illiquid assets, asset allocation and portfolio optimisation.
• Practical exposure to bond pricing, stochastic volatility modelling and Monte Carlo simulations.
• Proficient in time‑series analysis, econometrics and factor‑based modelling.
• Advanced Python (numpy, pandas) with production deployment experience.
• C++ highly valued; SQL proficiency; MS Office with VBA a plus.
• Clear communicator able to explain complex ideas to non‑specialists.