Quantitative Risk Analyst

ENGIE - International Supply & Energy Management; Courbevoie; Sur place; EUR 75 000 - 110 000; Plein temps

Résumé du poste

ENGIE Supply and Energy Management is seeking a Quantitative Risk Analyst to validate pricing models for derivatives and to design robust risk models across market, credit, and liquidity risks. You will implement validated models in C# within a large data and AWS/Dataiku environment, collaborating with traders, risk, and IT teams.

The role emphasizes rigorous quantitative analysis, model development, back-testing, and compliance, with a focus on energy commodity markets and cross-functional

Qualifications

• Master's degree or PhD in a quantitative discipline (Financial Engineering, Applied Mathematics, Statistics, Physics, Computer Science) with solid modeling analytics.
• At least three years of hands-on experience as a quantitative analyst developing, implementing, and challenging pricing and risk models in real-world environments.

Responsabilités

• Validate pricing models for complex derivatives and market/credit risk factors.
• Implement stochastic models in C# within the risk methodologies pricing library.
• Build and test alternative models to benchmark valuation methods.
• Participate in internal model committees with theoretical and numerical justifications.
• Design risk metrics for market risks (VaR, SVaR, stress tests) and credit risks (CVA, DVA).
• Ensure full documentation of models and methods for traceability and regulatory compliance.

Connaissances

Stochastic models

Statistics

C# / Python

AWS / Dataiku

Commodity markets

Formation

MSc or PhD

Outils

AWS

Dataiku

Description du poste

Location

Paris La Défense

About ENGIE and Supply & Energy Management

ENGIE, a global leader in low-carbon energy and services, relies on its Global Business Unit Supply & Energy Management (GBU S&EM) to provide reliable, sustainable, and affordable energy to all its customers. This strategic unit optimizes the Group’s and clients’ production assets and designs tailored energy solutions for our 200,000 professional clients and 15 million consumers.

Quantitative Risk Analyst – Risk Methodologies & Pricing Models

Location: Paris La Défense

About ENGIE and Supply & Energy Management

ENGIE, a global leader in low-carbon energy and services, relies on its Global Business Unit Supply & Energy Management (GBU S&EM) to provide reliable, sustainable, and affordable energy to all its customers. This strategic unit optimizes the Group’s and clients’ production assets and designs tailored energy solutions for 200,000 professional clients and 15 million consumers.

The Global Business Unit Supply & Energy Management leverages ENGIE’s assets to deliver secure and sustainable energy to its B2B and B2C customers.

It uses its expertise in energy management to provide decarbonized electricity 24/7.

Joining Supply & Energy Management means becoming part of a team of over 10,000 passionate experts spread across 20 countries, all united by a shared mission: shaping a greener and more efficient energy future. Together, we push the boundaries of innovation to deliver decarbonized energy 24/7. Join us and be part of those shaping the energy of tomorrow!

Context

As a Quantitative Risk Analyst, you will be part of a team that sits at the intersection of financial modeling, risk management, and software development. Your responsibilities will include validating pricing models for derivatives and designing and continuously improving risk models for market, credit, and liquidity risks.

Your daily tasks will encompass three key areas:

• Advanced Financial Modeling: You will cultivate a critical mindset and challenge stochastic models through close interaction with traders and pricing model designers.
• Technical Development: You will implement and test models and their alternatives within a robust C# and Python codebase, integrated into a Big Data environment (AWS, Dataiku).
• Cross-Functional Collaboration: You will work closely with Risk, IT, and Front Office teams to transform complex problems into practical solutions, whether through ad hoc pricing tools, back-testing prototypes, or automated model monitoring systems.

Additionally, you will ensure the long-term robustness of models by calibrating reserves under historical probability, implementing performance metrics, and regularly reviewing model parameters. This role combines scientific rigor with technical creativity and places you at the heart of the risk management team, focusing on energy commodity markets.

Key responsibilities

• Perform model validation of pricing models for complex derivatives (e.g., exotic basket options, swing options, storage, renewables, …).
• Implement validated stochastic models in C# within the risk methodologies pricing library.
• Build and test alternative models to benchmark and assess the robustness of valuation methods.
• Participate in internal model committees by providing theoretical and numerical justifications.
• Design risk metrics for market risks (VaR, SVaR, quantitative stress tests), implement prototypes and present method specifications for future IS integration.
• Design risk metrics for credit risks (PFE, CVA, DVA), implement prototypes and present method specifications for future IS integration.
• Ensure full documentation of models and methods for traceability, auditability, and regulatory compliance.

PROFILE

You hold a Master’s degree or PhD in a quantitative discipline such as Financial Engineering, Applied Mathematics, Statistics, Physics, Computer Science, or a related field. Your academic background has equipped you with a solid foundation in modeling, analytics, and problem-solving. Beyond your studies, you bring at least three years of hand-on experience as a quantitative analyst, during which you’ve developed, implemented, and challenged pricing and risk models in real-world financial environments. You are now looking to take on a new challenge where your expertise can make a real impact — bridging cutting-edge theory with practical, high-stakes applications in trading and risk management.

Hard Skills

• Solid knowledge of stochastic models applied to derivative pricing and risk modeling.
• Strong background in statistics: hypothesis testing, time series analysis, regression models.
• Proficient in object-oriented programming (C#, Python), with the ability to independently develop models and prototypes.
• Familiarity with Big Data technologies, particularly Amazon Web Services (AWS) or Dataiku, is a plus.
• Previous experience in Commodity Markets is a strong advantage.

Soft Skills

• Strong analytical skills, conceptual thinking, and scientific rigor.
• Ability to communicate complex concepts clearly and effectively to both technical and non-technical stakeholders.
• Team spirit and ability to work collaboratively in a fast-paced and multidisciplinary environment.

LANGUAGES

• Fluent in English

Why join us?

Diversity & Inclusion

At ENGIE S&EM, we are committed to fostering a gender-neutral and inclusive environment where everyone’s potential can thrive.

All our positions are open to people with disabilities. If you need any reasonable accommodations during the recruitment process, please inform your recruiter — we will be happy to support you.

About ENGIE

ENGIE is a global leader in low-carbon energy and services, committed to accelerating the transition towards a carbon-neutral world. Our purpose (“raison d’être”) is clear: act to reduce energy consumption and develop cleaner, more sustainable solutions that balance economic performance with a positive impact on people and the planet.

We rely on our core businesses — gas, power, renewable energy, and services — to deliver competitive, tailored solutions to our customers worldwide.

With 96,000 employees and a strong network of partners and stakeholders, ENGIE is more than a company: we are a community of Imaginative Builders, working everyday to create harmonious, sustainable progress.

Business Unit: Supply & Energy Management

Division: EM RISK CONTROL

Legal Entity: ENGIE Global Markets S.A.S.

Company Name: Supply and Energy Management

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