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- Tasks: Develop and maintain advanced pricing models using Python for trading and risk systems.
- Company: Global trading and investment group with a focus on energy commodities and financial markets.
- Benefits: Competitive salary, opportunities for growth, and a dynamic work environment.
- Why this job: Shape the future of trading analytics and make a real impact in a high-performing team.
- Qualifications: Expert in Python with a strong background in quantitative development and derivatives pricing.
- Other info: Collaborative culture with opportunities to work on cutting-edge technology in finance.
The predicted salary is between 48000 - 72000 £ per year.
This global trading and investment group operates across energy commodities and financial markets combining advanced quantitative methods with large-scale technology infrastructure. Its London engineering hub plays a central role in developing analytics that power front-office trading pricing and risk systems across oil, power, gas and equity products.
Key Responsibilities
- Develop and maintain Python pricing and risk libraries covering vanilla and structured options across commodities and equities.
- Implement and calibrate models such as Black-Scholes, Heston, SABR and Monte Carlo-based approaches for structured instruments (APOs, CSOs, ULDs, P1X).
- Design and maintain volatility surface calibration workflows including interpolation, extrapolation and smoothing.
- Collaborate with quantitative researchers and data engineers to translate model specifications into robust production-grade code.
- Manage market data dependencies, proxy logic and curve handling for valuation and risk analytics.
- Enhance model performance, numerical stability and diagnostic visibility.
- Contribute to regression testing, benchmarking and CI/CD workflows in Python and AWS environments.
- Act as subject-matter expert for pricing models and valuation logic supporting risk and trading teams globally.
Skills and Experience
- Expert-level Python developer with strong experience in numerical computing (NumPy, SciPy, Pandas).
- Deep understanding of derivatives pricing theory, volatility modelling and stochastic calculus.
- Experience with calibration, curve bootstrapping and risk measures (Greeks, sensitivities, VaR).
- Background in pricing and risk models for commodities or equity derivatives.
- Familiarity with cloud-based compute environments (AWS ECS, Lambda, S3) and DevOps tools (Git, Jenkins, Docker/Kubernetes).
- Knowledge of C or C# for potential model integration advantageous.
- Ability to interpret and implement quantitative research efficiently and transparently.
Profile
- 5-10 years of experience in quantitative development or model engineering within trading, banking or commodities.
- Advanced degree (Masters or PhD) in Mathematics, Physics, Financial Engineering or a related quantitative field.
- Demonstrated delivery of robust pricing models and scalable production code.
- Strong analytical skills and precision in solving complex numerical problems.
- Excellent communicator comfortable bridging quantitative, technical and business perspectives.
Why This Role Matters
This position underpins the evolution of the firm's global pricing and risk architecture. The Quant Developer ensures model integrity, accuracy and scalability—contributing directly to trading performance and decision-making. It is an opportunity to shape how advanced pricing analytics are engineered and deployed across a high-performing global trading business.
Required Skills Python, AWS
Quantitative Developer employer: Lithe Consulting Ltd
Contact Detail:
Lithe Consulting Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Developer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a GitHub repository with your projects, especially those related to Python and quantitative methods. This gives potential employers a sneak peek into your coding style and problem-solving abilities.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of derivatives pricing and risk models. We recommend practicing coding challenges and discussing your thought process out loud to showcase your analytical skills.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Quantitative Developer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python expertise and experience with numerical computing. We want to see how your skills align with the role, so don’t be shy about showcasing your background in derivatives pricing and risk models!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about quantitative development and how your experience can contribute to our trading and risk systems. Let us know what excites you about this role!
Showcase Relevant Projects: If you've worked on any projects involving model calibration or cloud-based environments, make sure to mention them. We love seeing real-world applications of your skills, especially if they relate to commodities or equity derivatives.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at Lithe Consulting Ltd
✨Know Your Python Inside Out
As a Quantitative Developer, you'll need to showcase your expert-level Python skills. Brush up on libraries like NumPy, SciPy, and Pandas, and be ready to discuss how you've used them in past projects. Prepare to solve coding challenges on the spot, so practice writing clean, efficient code.
✨Master the Models
Familiarise yourself with key pricing models such as Black-Scholes and Heston. Be prepared to explain these models clearly and discuss their applications in real-world scenarios. You might even be asked to walk through a calibration process, so having a solid grasp of volatility modelling and stochastic calculus is essential.
✨Showcase Your Collaboration Skills
This role involves working closely with quantitative researchers and data engineers. Think of examples where you've successfully collaborated on projects, especially in translating complex model specifications into production-grade code. Highlight your communication skills and how you bridge technical and business perspectives.
✨Get Comfortable with Cloud and DevOps
Since familiarity with AWS and DevOps tools is crucial, make sure you can discuss your experience with cloud-based environments and CI/CD workflows. Be ready to talk about how you've integrated tools like Git, Jenkins, or Docker in your previous roles, and how they contributed to enhancing model performance and stability.
