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PayPal Sr Machine Learning Engineer France, Auvergne-Rhône-Alpes 316438206
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• Strong proficiency in Python for data analysis, machine learning, and automation.
• Solid understanding of supervised and unsupervised AI/machine learning methods (e.g., XGBoost, LightGBM, Random Forest, clustering, isolation forests, autoencoders, neural networks, transformer-based architectures).
• Experience in payment fraud, AML, KYC, or broader risk modeling within fintech or financial institutions.
• Experience developing and deploying ML models in production using frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
• Hands-on experience with LLMs (e.g., OpenAI, LLaMA, Claude, Mistral), including use of prompt engineering, retrieval-augmented generation (RAG), and agentic AI to support internal automation and risk workflows.
• Ability to work cross-functionally with engineering, product, compliance, and operations teams.
• Proven track record of translating complex ML insights into business actions or policy decisions.
• Solid understanding of supervised and unsupervised AI/machine learning methods (e.g., XGBoost, LightGBM, Random Forest, clustering, isolation forests, autoencoders, neural networks, transformer-based architectures).
• Experience in payment fraud, AML, KYC, or broader risk modeling within fintech or financial institutions.
• Experience developing and deploying ML models in production using frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
• Hands-on experience with LLMs (e.g., OpenAI, LLaMA, Claude, Mistral), including use of prompt engineering, retrieval-augmented generation (RAG), and agentic AI to support internal automation and risk workflows.
• Ability to work cross-functionally with engineering, product, compliance, and operations teams.
• Proven track record of translating complex ML insights into business actions or policy decisions.
Essential Responsibilities:
- Develop and optimize machine learning models for various applications.
- Preprocess and analyze large datasets to extract meaningful insights.
- Deploy ML solutions into production environments using appropriate tools and frameworks.
- Collaborate with cross-functional teams to integrate ML models into products and services.
- Monitor and evaluate the performance of deployed models.
Minimum Qualifications:
- Minimum of 5 years of relevant work experience and a Bachelor's degree or equivalent experience.
- Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
- Familiarity with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
- Several years of experience in designing, implementing, and deploying machine learning models.
Our Benefits:
Any general requests for consideration of your skills, please
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