Lead Data Scientist

Overview

Lead data science, Generative AI and Agentic AI initiatives, designing and building AI-driven solutions using LLMs, RAG, AI agents, machine learning and advanced analytics, and guiding junior team members.

What you'll do

• Design, build and optimize LLM-based applications using prompt engineering, RAG, embeddings, vector search and AI agents.
• Develop and evaluate machine learning models for prediction, classification, forecasting, recommendation, anomaly detection, NLP and optimization.
• Work with structured and unstructured data to extract insights, build features and create AI-ready data pipelines.
• Evaluate GenAI and Agentic AI outputs for accuracy, relevance, hallucination risk, safety, bias, latency and business usefulness.
• Leverage AI-assisted coding tools such as Codex, GitHub Copilot, ChatGPT, Cursor or similar for coding, experimentation, debugging and documentation.
• Communicate technical findings, model performance, risks and recommendations clearly to stakeholders.

What you'll need

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Statistics, Mathematics, Engineering or a related field.
• Minimum 4+ years of hands-on experience in data science, machine learning, AI, analytics or related roles.
• Strong programming skills in Python and strong working knowledge of SQL.
• Hands-on experience with data science libraries such as Pandas, NumPy, Scikit-learn and Matplotlib, or similar.
• Strong understanding of machine learning algorithms, model evaluation, feature engineering, experimentation and statistical analysis.
• Practical experience with Generative AI concepts, including LLMs, prompt engineering, embeddings, RAG, vector databases and model evaluation.
• Understanding of Agentic AI concepts, including tool calling, planning, memory, orchestration, multi-agent workflows and guardrails.
• Experience working with APIs and integrating AI/ML models into applications, workflows or business systems.
• Ability to understand, validate, debug and improve outputs generated by GenAI tools.

Nice to have

• Experience with LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen or similar.
• Familiarity with MLOps and LLMOps practices including model monitoring, prompt versioning, evaluation pipelines, CI/CD and observability.