Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Quantitative Risk Analyst - Commodities

Millennium Partners
London
3 days ago
Create job alert

The Quant Risk Analyst will help analyze and monitor the Millennium's Commodities risk, build quantitative models for performance and risk analysis, and participate in the implementation of ad-hoc simulation models for risk measurement (e.g., VaR improvement, scenario analysis, etc.).

Principal Responsibilities
  • Build data analysis models to identify patterns in portfolio managers' performance and highlight top pnl and risk drivers (e.g., factor models, risk decomposition).
  • Design and implementation of risk and scenario GUI/visualization tools (dashboards).
  • Development of option pricing and volatility models in partnership with the Quant Technology team.
  • Handling of large data sets, using machine learning techniques to enhance traditional risk measures.
  • The role will collaborate with risk managers across asset classes as well as various technology and data teams within the firm, capturing requirements, and monitoring delivery.
  • Regular interaction with portfolio managers across Europe and Asia.
Requirements
  • Masters or PhD level training in a quantitative field, e.g., Engineering, Computer Science, Mathematics, or Physics.
  • Minimum 3 years of professional experience in Trading, Structuring, Risk, or Quant role, in a Financial institution, Fintech, Trading house, or Commodities house.
  • Strong coding skills required: Python, proficiency in data science stack (Pandas, scikit-learn, or equivalent), SQL. Familiarity with GUI development (Dash, Panel, or equivalent).
  • Experience designing, developing, and deploying trading tools and GUIs, and at least one of the following: risk models, option pricers, alpha signals, portfolio optimizers, trading algorithms.
  • Experience in an alpha research, portfolio optimization, Commodities, or Trading environment is a plus.
  • Candidate will need to fit into the active culture of Millennium, judged by the ability to deliver timely solutions to risk management issues within the firm.
  • Entrepreneurial inclination: ability to work alone and act as a project manager.
  • Strong communication skills, both written and verbal.
  • Good team player - one who is able to prioritize in a fast-moving, high-pressure, constantly changing environment.
  • Ability to work with Portfolio Managers and foster collaborative relationships.


#J-18808-Ljbffr

Related Jobs

View all jobs

Quantitative Risk Analyst

Quantitative Risk Analyst

Quantitative Risk Analyst

Quantitative Risk Analyst - Commodities

Quantitative Business Analyst – Risk Technology (PFE / Credit Risk) (m/f/d)

Algorithmic Trading Model Risk Quantitative Analyst (Associate)

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.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

Why the UK Could Be the World’s Next Data Science Jobs Hub

Data science is arguably the most transformative technological field of the 21st century. From powering artificial intelligence algorithms to enabling complex business decisions, data science is essential across sectors. As organisations leverage data more rapidly—from retailers predicting customer behaviour to health providers diagnosing conditions—demand for proficiency in data science continues to surge. The United Kingdom is particularly well-positioned to become a global data science jobs hub. With world-class universities, a strong tech sector, growing AI infrastructure, and supportive policy environments, the UK is poised for growth. This article delves into why the UK could emerge as a leading destination for data science careers, explores the job market’s current state, outlines future opportunities, highlights challenges, and charts what must happen to realise this vision.