Lead Data Scientist

Machine Learning & Data Science

• Lead the design, development, and deployment of advanced Machine Learning solutions to solve complex business and operational challenges.

• Develop and optimize models across areas such as classification, clustering, anomaly detection, forecasting, recommendation systems, and predictive analytics.

• Drive the full model lifecycle from data exploration and feature engineering through experimentation, evaluation, deployment, and monitoring.

• Define model performance metrics, validation frameworks, and success criteria to ensure business relevance and measurable impact.

• Identify opportunities to leverage data and AI to improve operational efficiency, automation, decision-making, and user experience.

• Guide best practices in statistical analysis, experimentation, model governance, and AI quality assurance.

• Mentor and provide technical leadership to Data Scientists and AI practitioners across the organization.

Generative AI & Agentic AI

• Lead the design and implementation of Generative AI solutions leveraging Large Language Models (LLMs), embeddings, Retrieval Augmented Generation (RAG), and multi-agent architectures.

• Develop AI agents capable of reasoning, planning, knowledge retrieval, and task orchestration within complex enterprise environments.

• Evaluate emerging AI technologies, models, and frameworks to identify opportunities for innovation and business value creation.

• Design and optimize prompt engineering, evaluation methodologies, guardrails, and AI safety mechanisms.

• Define architectures for agentic systems integrating enterprise data, business workflows, APIs, and knowledge repositories.

• Establish evaluation frameworks to measure accuracy, reliability, explainability, and operational readiness of GenAI and Agentic AI solutions.

• Drive proof-of-concepts and pilot initiatives from ideation through industrialization and production adoption.