About the TeamThe AI Center of Excellence leverages advanced machine learning, LLMs, and agentic systems to enhance threat detection and automate security workflows for our global customers. We transform 20+ years of threat intelligence into proactive defense features through a collaborative, research-driven engineering environment.
About the Role
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.
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.
The skills and qualities you’ll bring include:
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.
We know that the best ideas and solutions come from multi-dimensional teams. That’s because these teams reflect a variety of backgrounds and professional experiences. If you are excited about this role and feel your experience can make an impact, please don’t be shy – apply today.
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