Quantitative Investment Analyst & Trade Execution

Black Swan Group

West End

Hybrid

GBP 60,000 - 75,000

Full time

26 hours ago
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Benefits offered by this job

Hybrid working
Office-based desk

Job summary

Black Swan Group in London is seeking a Quantitative Investment Analyst to join the Front Office team. You will build investment models, conduct quantitative research, and support trade execution across FX, equities and fixed income.

Strong Python skills and a quantitative mindset are essential for this office-based role. The position is described as an Analyst grade (2–4 years experience) with a hybrid working pattern: three days in the office and two days remote, evolving to five days in the

Qualifications

  • Mathematical degree with 2.1 or higher from a reputable university.
  • Advanced Python programming skills for quant modeling.
  • 2–4 years of Quantitative Investment Analyst experience.
  • Not a path to PM/trader in first 4 years; office-based with some hybrid work.

Responsibilities

  • Construct investment solutions and manage quantitative models.
  • Support trade execution for FX, equities and fixed income.
  • Perform Morningstar-like analysis and stock/fund evaluation.
  • Develop quant tools and data analysis capabilities.

Skills

Python programming
Quantitative analysis
Financial modeling
Investment research

Education

Mathematical degree (2.1+)
Masters or PhD in Mathematics

Job description

  • Location ~ London (West End)
  • Compensation ~ £60,000 to £75,000 Base Salary (GBP) + benefits + bonus (*Discretionary Fund Performance Bonus)
  • ***Right to Work in the UK - *NO SPONSORSHIP OFFERED – No PSV (Post Study Visas) & Dependant Visa will not be considered (time limited and 3rd party risk)***
  • ***WE ASK CANDIDATES TO RESPECT THIS REQUIREMENT***

A growing Asset Management firm based in London is looking to hire into its Front Office Quantitative Investment Team to service growth in investment and assets under management.

  • Reporting directly into the Quantitative CIO (Chief Investment Officer) and Head Quant PM.
  • Quantitative Investment Analyst will work with the Quantitative Investment team to construct investment solutions and model management.
  • The role will also entail Quant Trade Execution and is an “Analyst” (2-4 Years ONLY of experience) grade hire for the business.
  • Products - Covering FX, Equites and Fixed Income Quant trade execution, reviewing systematic investments, and executing trades in the most cost effective and efficient manner.
  • Conducting Morningstar analysis as part of a comprehensive Quant financial research approach, reviewing system, evaluation of stocks, mutual funds, and exchange-traded funds to determine their intrinsic value, risk levels, and market standing.
  • Covering Stock evaluation, fund screening, and initial investment model construction, balancing and maintenance.
  • PYTHON CODING – to build data analysis and quantitative investing tools. Please note a python test will be part of the interview process.

Prerequisite Required Experience - ~ Quantitative Investment Analyst & Trade Execution–Analyst (2-4 Years ONLY)

  • Mathematical Degree – 2.1 and above from Russel Group University – OR Masters or PHD in Mathematics – this is prerequisite given mathematical and numerical aspects of the role.
  • Advanced Python Programming Skills – direct competency in building quant models, pricing tools and quantitative execution.
  • 2 to 4 years experience (MAX) Quantitative Investment Analyst experience – from Asset Manager / Investment Bank or Hedge Fund.
  • THIS ROLE IS NOT SUITED FOR ANYONE WANTING TO BE A PM – this is Quantitative Investment Analyst role, and you will cover some execution.
  • Please note no pathway to become a trader or PM in the first 4 years of this role– minimum criteria for the desk and wanted to set expectations.
  • Working Model – in office, investment desk based and some scope for hybrid working.
  • Business has (3 days in the office and 2 days work from home)
  • GIVEN THIS IS FRONT OFFICE - post 1st 6mths – 5 days in the office, due to learning and getting up to speed)
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