This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
As a Data Scientist (SE-2), your primary responsibility will be to contribute to the end-to-end development, evaluation, and monitoring of ML and LLM-based security features.
Job Responsibility:
Execute data acquisition, cleaning, and feature engineering to prepare high-quality datasets for security modeling
Build and evaluate supervised and unsupervised ML models, including classification, clustering, and anomaly detection
Develop and optimize LLM-based workflows, including prompt engineering and the implementation of RAG pipelines
Support the deployment and observability of models on AWS infrastructure using established CI/CD pipelines
Requirements:
A courageous and curious mindset, demonstrating a strong ability to learn new technologies and operate in ambiguous problem spaces
Exceptional collaboration skills with the ability to work cross-functionally with senior scientists and engineers to ship production features
Strong ownership and principled decision-making when evaluating model performance and data quality
2–5 years of professional experience in Data Science or ML Engineering roles
Proficiency in Python and its scientific ecosystem, specifically Pandas, NumPy, and scikit-learn
Hands-on experience building and tuning supervised and unsupervised machine learning models
Working knowledge of AWS ML services, including SageMaker, S3, Bedrock, and Lambda
Foundational exposure to LLM orchestration frameworks such as LangChain or HuggingFace Transformers
Understanding of deep learning frameworks (PyTorch or TensorFlow) for NLP or sequence-based problems
Familiarity with model evaluation metrics and explainability techniques like SHAP or LIME
Basic understanding of CI/CD pipelines (GitHub Actions/Jenkins) and version control for ML workloads
Experience monitoring model performance and drift using tools like CloudWatch