Principal Data Scientist: Associate AI experience

What you'll do

• Architect and deploy end-to-end ML systems including data pipelines, feature stores, and production serving layers.
• Build and ship autonomous AI agents using LangChain, Pydantic AI, and RAG patterns.
• Design agentic evaluation frameworks to benchmark performance across task completion and multi-step reasoning.
• Deploy enterprise-scale NLP pipelines using BERT/SBERT embeddings and vector search.
• Lead high-impact projects from initial problem framing and prototyping through to production rollout.
• Mentor senior scientists and engineers on advanced modeling techniques and experimentation design.
• Establish best practices for model validation, experimentation, and safe deployment of AI systems.
• Present technical findings and quantified business impacts to senior leadership.

What we're looking for

• Master's degree in a quantitative field and 3 years of experience in an analytics related field, or a Bachelor's degree and 5 years of experience.
• 7 years of experience in an analytics or related field is also accepted as a minimum qualification.
• Deep expertise in ML algorithms (XGBoost, CatBoost, LightGBM, Random Forest, AutoML) and deep learning (PyTorch, transformer architectures).
• Production experience with NLP (BERT, SBERT, FAISS, RAG), computer vision (YOLO, CLIP), and time series forecasting (ARIMA, Prophet).
• Hands-on experience building and orchestrating multiple AI agents with LangChain, RAG, tool integration, and memory management.
• Multi-cloud ML platform proficiency in AWS SageMaker and GCP (Vertex AI, BigQuery, BigQuery ML).
• Strong Python skills (Pandas, NumPy, Scikit-learn) with containerization (Docker) and MLOps practices.
• PhD in a related field, publications, or active peer review experience (preferred).