Quantitative Analyst

Mesirow
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Analyst

Quantitative Analyst (Equities & Equity Derivatives - VP)

Quantitative Analyst

Quantitative Analyst

Quantitative Analyst

Quantitative Analyst (Equities & Equity Derivatives - VP)

Mesirow is an independent, employee-owned financial services firm founded in 1937. Headquartered in Chicago, with offices across the country, Mesirow serves clients through capabilities spanning Private Capital & Currency, Capital Markets and Investment Banking, and Advisory Services.


The role of the Quantitative Analyst is to evaluate and analyze currency markets. This includes quantitative financial analysis and in-depth knowledge of financial modeling.


DESCRIPTION

We are seeking a highly motivated Quantitative Researcher to join our Currency group. The role involves conducting cutting‑edge research on global FX markets, designing and implementing systematic trading strategies, and contributing to the development of robust financial models and research infrastructure. You will collaborate closely with portfolio managers, traders, and technology teams to generate new investment ideas and deliver innovative solutions in a fast‑moving environment.


This position is open to both experienced researchers and recent PhD graduates with a strong quantitative background, proven programming ability, and a passion for applying advanced analytics to global markets.


PRIMARY DUTIES AND RESPONSIBILITIES

  • Conduct quantitative research and analysis of global currency markets
  • Design, develop, and maintain financial models to support market analysis and trading strategies
  • Build and enhance systematic platforms in multiple programming languages, replicating existing models to validate performance and integrating new models for evolving market conditions
  • Generate and refine new investment ideas, including structured products and other innovative solutions for the Currency group
  • Prepare and deliver clear presentation materials for clients and senior management
  • Collaborate on the development and enhancement of research infrastructure, systematic processes, and trading tools, partner closely with traders, portfolio managers, and technology teams to translate research insights into executable strategies
  • Support ad hoc projects and strategic initiatives as required

QUALIFICATIONS

  • PhD or advanced degree in econometrics, mathematics, statistics, machine learning, or a related quantitative field
  • Strong programming skills in Python with demonstrated experience handling and analyzing large datasets, experience with scientific computing libraries (NumPy, pandas, SciPy, TensorFlow/PyTorch), familiarity with cloud environments, distributed computing, or database technologies (SQL)
  • Knowledge of foreign exchange products, including spot, forwards, NDFs, swaps, and options
  • Background in developing and enhancing research, with proven record of independent research, evidenced by publications in top journals and presentations at leading conferences
  • Leverage machine learning to extract predictive signals from structured and unstructured financial datasets, including the development of end‑to‑end ML pipelines
  • Excellent communication skills, with the ability to convey complex ideas to both technical and non‑technical stakeholders

EOE

EOE


Seniority level

Associate


Employment type

Full-time


Job function

Finance


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.