Quant Developer / Intraday Quant Trader (f/m)
Engie is hiring a Quant Developer / Intraday Quant Trader (f/m). Level rates it ; you can apply on Engie.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include PyTorch, TensorFlow and scikit-learn.
- Level lists 688 other open roles at Engie.
AI in this role
Quant Developer / Intraday Quant Trader (f/m)
📍 Katowice | Hybrid
🚀 About the Role
We are looking for someone who combines analytical thinking, programming, and strategy development.
Your role will be to turn ideas into working models and algorithms — from concept, through testing, to deployment in real systems.
This is a position for people who enjoy solving complex problems, working with data, and building end‑to‑end solutions.
Responsibilities
- Design and develop data‑driven strategies and quantitative models
- Build and implement algorithms in Python
- Develop predictive models (statistical, ML / DL-based)
- Analyze data and identify patterns / signals
- Perform backtesting and validate strategies
- Iteratively improve models based on performance
- Build tools supporting automated decision‑making
- Work with large datasets and real-time data pipelines
Candidate Profile
- Degree in a quantitative field (math, physics, engineering, CS, or similar)
- Strong Python skills (must-have)
- Experience with SQL and working with structured data
- Experience in data analysis, modelling, or algorithm development
- Solid understanding of statistics and time-series analysis
- Familiarity with Machine Learning / Deep Learning concepts:
- regression / classification
- tree-based models (XGBoost, LightGBM, CatBoost)
- basic neural networks (feedforward, LSTM / time-series models)
- Experience with libraries such as pandas, numpy, scikit-learn, PyTorch / TensorFlow
- Strong problem-solving mindset and system thinking
- English (B2+)
Nice to Have
- Experience with neural networks applied to time-series / forecasting
- Experience with optimization techniques and hyperparameter tuning
- Working with real-time / high-frequency data
- Experience deploying models into production (APIs, pipelines)
Who We’re Looking For
Someone who:
- enjoys breaking down complex problems
- can turn ideas into working algorithms
- tests hypotheses instead of relying on intuition
- iterates quickly (build → test → improve)
- is curious about models and why they work (or don’t)
We Offer
- Direct impact on strategies and systems
- Fast iteration cycles and real implementation
- Opportunity to build end‑to‑end solutions
- Growth in quant / algo / data / ML
At ENGIE, every talent has a role to play in accelerating the energy transition.
Make a difference and enjoy a fulfilling professional experience. Take on exciting challenges and build a career path that reflects who you are.
Join us and be part of the adventure of the century!
How we rate this
Quant Developer / Intraday Quant Trader (f/m) at Engie rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
- What are the limits of scikit-learn that you've run into, and how did you work around them?
- What's a project where you used XGBoost hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: PyTorch, TensorFlow, scikit-learn, and XGBoost. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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