Lead Data Scientist
On-siteParis, Île-de-France, France
Job Summary
Contribute to the research, prototyping, and production of new AI and ML features across the Fraud, Identity, and Financial Crime Compliance portfolio. Partner with Product Managers and Engineering teams to design and deliver impactful enhancements, while deeply understanding existing products to identify AI-driven improvement opportunities. Design and execute experiments to validate research ideas, train and optimize LLM and ML models on structured and unstructured data, and develop strategies for real-time model inference and scalable deployment. Collaborate with external vendors for data initiatives and engage with customers to integrate evolving fraud patterns into product development, supporting other data scientists to foster technical excellence. Work primarily on a European schedule, collaborating across time zones and traveling occasionally.
Required Qualifications
- Master's degree or PhD in Computer Science, Artificial Intelligence, Applied Mathematics, or a related field
- 4+ years of experience building, training, and evaluating Deep Learning and Machine Learning models using tools such as PyTorch, TensorFlow, scikit-learn, HuggingFace, or LangChain
- Strong programming skills in Python, including data wrangling, analysis, and visualization
- Solid experience with SQL and database querying for data exploration and preparation
- Proven ability to tackle ambiguous problems, develop data-informed strategies, and define measurable success criteria
- Strong English proficiency (C1/C2) and proven experience working in multicultural, international environments
- Ability to collaborate across time zones and travel occasionally as required
Desired Qualifications
- Degree from a leading Engineering School (Grand Ecole) or University with a strong quantitative curriculum is highly valued
- Experience in a start-up or a cross-functional team is a plus
- Experience in Natural Language Processing (NLP) is a plus
- Familiarity with object-oriented or functional programming languages such as C++, Java, or Rust is a plus
- Experience with software engineering tools and practices (e.g. Docker, Kubernetes, Git, CI/CD pipelines) is a plus
- Knowledge of ML Ops, model deployment, and monitoring frameworks
- Understanding of fraud prevention, authentication, or identity verification methodologies is a plus
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