Quantitative / Machine Learning Engineer

TOTAL Deutschland GmbH; Genf; Vor Ort; CHF 110.000 - 170.000; Vollzeit

Zusammenfassung

TotalEnergies is seeking a Quantitative / Machine Learning Engineer to join its trading tech team in Geneva. You will develop models for European power markets, bridging research, back-testing and live deployment while collaborating with analysts, traders and IT staff.

The role emphasizes production-quality ML, time-series analysis and robust software practices, with exposure to fast-paced energy trading and cross-functional projects across TotalEnergies’ European activities.

Qualifikationen

• Engineering degree, Master's degree or PhD in a quantitative field such as Mathematics, Physics, Machine Learning, Computer Science or a related discipline.
• Approximately 2–3 years of professional experience working with machine-learning models in production, ideally with live forecasting or real-time applications.
• Strong proficiency in Python.
• Experience with MongoDB, SQL and Shell scripting.
• A solid understanding of software architecture and production-quality development practices.
• Experience with Docker and familiarity with DevOps/MLOps environments, including CI/CD pipelines.
• Strong knowledge of machine-learning techniques, including supervised and unsupervised learning.
• Practical experience with time-series modelling and analysis.
• Ability to analyse complex systems and identify direct and indirect relationships between multiple signals and datasets.
• Strong communication skills and the ability to explain quantitative results clearly to both technical and business stakeholders.
• The ability to work effectively in a fast-paced, collaborative trading environment.

Aufgaben

• Develop quantitative and machine-learning models for algorithmic trading in European power markets.
• Build models across the full lifecycle, from data preparation and feature engineering through model training, optimization, validation and live deployment.
• Design new indicators, signals and features to improve quantitative and machine-learning trading models.
• Analyse large and complex datasets to identify patterns, relationships and trading signals.
• Conduct quantitative research on European power markets and translate market observations into actionable modelling ideas.
• Monitor the performance and robustness of models in both simulated and live trading environments.
• Continuously assess model behaviour and identify opportunities to improve forecasting accuracy and trading performance.
• Contribute to rigorous back-testing, validation and model-performance methodologies.
• Work with cross-functional teams to bring models into production and ensure their reliability, scalability and maintainability.
• Collaborate closely with algorithmic traders and technology teams to turn quantitative research into operational trading solutions.
• Contribute your data science expertise to broader modelling and optimization topics within the team.

Kenntnisse

Python

Time-series analysis

Analytical thinking

Communication skills

Team collaboration

Ausbildung

Engineering degree or higher in Mathematics, Physics, ML, CS or related

Tools

MongoDB

SQL

Shell scripting

Docker

CI/CD

Jobbeschreibung

TotalEnergies is developing its activities across the power value chain, with a growing portfolio of renewable generation, flexible assets and power supply activities across Europe.

Within Trading & Shipping, our algorithmic trading teams are building advanced quantitative and machine-learning solutions to support trading and optimization activities in European short-term power markets.

As a Quantitative / Machine Learning Engineer, you will join a highly technical and collaborative environment at the intersection of energy trading, quantitative modelling, machine learning and software engineering.

You will contribute directly to the development of models and algorithms used to identify and capture opportunities in European power markets, working closely with quantitative analysts, algorithmic traders, data scientists and IT specialists.

The role offers significant exposure to fast-moving energy markets and the opportunity to work on models that move from research and back-testing through to live production and ultimately contribute to trading performance.

You have a strong quantitative background and enjoy applying advanced modelling techniques to complex, real-world problems.

We are looking for someone with:

• An engineering degree, Master's degree or PhD in a quantitative field such as Mathematics, Physics, Machine Learning, Computer Science or a related discipline.
• Approximately 2–3 years of professional experience working with machine-learning models in production, ideally involving live forecasting or other real-time applications.
• Strong proficiency in Python.
• Experience with MongoDB, SQL and Shell scripting.
• A solid understanding of software architecture and production-quality development practices.
• Experience with Docker and familiarity with DevOps/MLOps environments, including CI/CD pipelines.
• Strong knowledge of machine-learning techniques, including supervised and unsupervised learning.
• Practical experience with time-series modelling and analysis.
• The ability to analyse complex systems and identify direct and indirect relationships between multiple signals and datasets.
• Strong communication skills and the ability to explain quantitative results clearly to both technical and business stakeholders.
• The ability to work effectively in a fast-paced, collaborative trading environment.

Additional Assets

The following would be considered an advantage:

• Previous exposure to energy, commodities or financial markets.
• Understanding of European power markets, power generation or energy supply mechanisms.
• Experience working with trading signals, forecasting models or optimization problems.
• Familiarity with project-management and collaborative tools such as Jira, Asana or Monday.com.

Why Join Us?

This role offers the opportunity to work at the forefront of data science and algorithmic trading within the energy sector.

You will work on advanced quantitative models with a direct connection to live markets and trading decisions, while collaborating with experienced professionals across trading, quantitative research and technology.

You will join an international environment where machine learning and quantitative analysis play an increasingly important role in optimizing TotalEnergies' activities across European power markets.

As part of the Algorithmic Trading team, you will:

• Develop quantitative and machine-learning models supporting algorithmic trading strategies in European power markets.
• Build models across the full lifecycle, from data preparation and feature engineering through model training, optimization, validation and live deployment.
• Design new indicators, signals and features to improve quantitative and machine-learning trading models.
• Analyse large and complex datasets to identify patterns, relationships and trading signals.
• Conduct quantitative research on European power markets and translate market observations into actionable modelling ideas.
• Monitor the performance and robustness of models in both simulated and live trading environments.
• Continuously assess model behaviour and identify opportunities to improve forecasting accuracy and trading performance.
• Contribute to rigorous back-testing, validation and model-performance methodologies.
• Work with cross-functional teams to bring models into production and ensure their reliability, scalability and maintainability.
• Collaborate closely with algorithmic traders and technology teams to turn quantitative research into operational trading solutions.
• Contribute your data science expertise to broader modelling and optimization topics within the team.

TotalEnergies Gas & Power (TGP) is the trading arm of TotalEnergies in the field of low-carbon energies. With more than 550 employees, TGP operates in Switzerland, the United States, Singapore, France, Germany and Spain. It evolves in fast-changing market dynamics influenced by internal and external factors that require constant adaptation. TGP is responsible for optimizing the purchase and sales portfolios of gas, electricity, CO₂ emissions, dry bulk and LNG across global markets, in compliance with the Company’s trading guidelines.