Principal Data Scientist
Primary Skills
---Principal Data Scientist + Agentic AI
---Lead Data Scientist + LangChain OR LangGraph
---LLM + fine-tuning + "production
---multi-agent OR autonomous agents + LLM"
---RAG pipeline
---GenAI + leadership + "MLOps"
Specialization
1. Agentic AI (multi-agent workflows, autonomous agents, tool-using agents)
2. LLMs (fine-tuning, prompt engineering, RAG pipelines, production deployment)
3. LangChain / LangGraph / LlamaIndex / CrewAI / AutoGen
4. MLOps / LLMOps (model monitoring, CI/CD, versioning)
Job requirements
The Principal Data Scientist will play a critical role in translating complex business problems into scalable, AI-driven solutions. This role demands strong leadership, deep expertise in Agentic AI, Large Language Models (LLMs), and advanced analytics, along with the ability to influence senior stakeholders and drive measurable business outcomes.
Core Responsibilities
- Problem Formulation: Translate business objectives into well-defined data science, ML, and Agentic AI problems; validate OKRs using robust statistical and experimental measures.
- Agentic AI & LLM Solutions: Design, build, deploy, and optimize Agentic AI systems (multi-agent workflows, task orchestration, autonomous decision-making) using LLMs for real-world enterprise use cases.
- LLM Development & Deployment: Fine-tune, prompt-engineer, evaluate, and productionize LLMs (open-source or proprietary) for use cases such as copilots, RAG pipelines, conversational AI, and intelligent automation.
- Data Wrangling & Feature Engineering: Handle structured and unstructured data at scale, including text, documents, and conversational data for LLM-powered solutions.
- Insight Generation & Data Storytelling: Convert complex analytical outputs and AI model results into clear, compelling narratives for business and executive audiences.
- Technical Decision-Making: Make informed trade-offs on model complexity, iteration depth, experimentation cycles, and time-to-value.
- Design Thinking & Innovation: Apply design thinking principles to build user-centric AI products and data solutions.
- Mentorship & Leadership: Coach senior data scientists, review architectures, and establish best practices across data science, ML, and GenAI initiatives.
Key Qualifications – Technical Expertise
