Data Scientist

Experience: 3–5 years as a Data Scientist (or similar role), with a proven track record of shipping models to production environments.

Python Pro: Strong coding skills with a deep understanding of software engineering best practices (clean code, CI/CD, testing).

DS Foundations: Deep theoretical and practical knowledge of Data Science models from classical machine learning (Trees, Boosting, Regression) to deep learning architectures. You know how to choose the right tool for the job, not just the trendiest one.

Infrastructure Savvy: Comfortable with the "ops" side of data science hands-on experience with Docker, Kubernetes, and building scalable systems.

Tooling: Proficient in the standard stack (Pandas, scikit-learn) and modern deep learning frameworks (PyTorch or TensorFlow).

Cloud Native: Experienced in building and deploying within major cloud ecosystems (AWS, GCP, or Azure).

Bonus Points (Nice to Have):

Advanced RAG: Experience building and optimizing RAG (Retrieval-Augmented Generation) pipelines, including advanced retrieval strategies and reranking.

Agentic Workflows: Familiarity with Agentic RAG architectures (using frameworks like LangChain, LlamaIndex, or CrewAI) to handle complex, multi-step reasoning tasks.

Vector Infrastructure: Knowledge of vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search optimization.