Jobster
Job Description
Position: Quantitative Analyst – Asset-Backed Finance (Hybrid)
A prestigious London-based investment management firm is actively recruiting a Quantitative Analyst / Data Scientist to bolster their Credit & Asset-Backed Finance division. This specialized role centers on the architecting of sophisticated quantitative frameworks and the execution of granular data investigations to guide high-stakes capital allocation.
The successful applicant will be responsible for transforming complex raw data into actionable intelligence, specifically focusing on the performance and valuation of credit-linked assets. Candidates should possess a Master’s degree or Doctorate in a mathematically rigorous field, alongside a minimum of 3 years of professional experience within the financial sector. Advanced mastery of Python for financial modeling and data manipulation is mandatory. This opportunity provides a modern hybrid work structure, combining office-based collaboration with remote flexibility, and features a primary salary reaching £100,000 complemented by an annual performance incentive.
Job Data Table
| Category | Details |
| Company Name | Jobster (On behalf of an Investment Firm) |
| Location | London, United Kingdom |
| Locality | London Finance Hub |
| Country | United Kingdom |
| City | London |
| Region | Europe |
| Job Type | Full-time |
| Salaries | Up to £100,000 a year |
| Experience Level | 3+ Years |
| Travel | Minimal |
| Language | English |
| Benefits | Performance bonus, Hybrid work model |
Skills & Competency Table
| Key Skill | Level |
| Python Programming | Expert |
| Credit & Asset-Backed Modeling | Expert |
| Quantitative Data Science | Expert |
| Statistical Analysis | Advanced |
| Fixed Income / Credit Knowledge | Advanced |
| Machine Learning (Financial) | Intermediate |
Salaries Pay Calculator Table
| Annual Salary | Bonus (Est.) | Total Compensation | Notes |
| £85,000 | £15,000 | £100,000 | Baseline for 3 years experience. |
| £100,000 | £25,000 | £125,000 | Maximum basis for the advertised role. |
| £120,000 | £40,000+ | £160,000+ | Market tier for senior ABF quants in London. |
Job Summary
This London-based role involves developing quantitative models and performing data science to support a Credit & Asset-Backed Finance team. You will drive investment decisions through rigorous data analysis. Requires a Master’s/PhD, 3+ years of experience, and expert Python skills. The position follows a hybrid model with a £100k base.
5 FAQs
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What is the required educational background? A Master’s or PhD in a quantitative discipline is expected.
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Is Python required for this position? Yes, proficiency in Python is a core requirement for modeling and analysis.
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What is the work-from-home policy? The firm offers a hybrid model, balancing office time and remote work.
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Does the role focus on a specific asset class? Yes, it is centered on Credit and Asset-Backed Finance (ABF).
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What is the salary ceiling for the base pay? The base salary goes up to £100,000 per year plus a bonus.
Expert Analysis
In 2026, Asset-Backed Finance (ABF) quants are prioritizing Collateral Performance Analytics over traditional static models. With the rise of private credit, the ability to ingest non-traditional data via Python to forecast cash-flow volatility in consumer or commercial loan pools is the primary differentiator for investment firms looking to outperform the market.
Location & Logistics Guide
The role is located in London, the global epicenter for fixed income and structured finance. Most firms operate out of the City of London or Canary Wharf . The hybrid model allows for a commute that leverages the extensive London Underground and Elizabeth Line networks. Wikipedia URL:https://en.wikipedia.org/wiki/London
Career Path
A Quantitative Analyst in ABF can progress to Lead Portfolio Strategist , Head of Credit Research , or Director of Quantitative Research . Given the data science focus, paths also lead into AI-driven Investment Management or specialized leadership roles in Alternative Asset Management and private debt markets.
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