Lead Data Scientist

Responsibilities

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.

Profile required

Education & Experience

• 9+ years of experience in Data Science, Machine Learning, or Applied AI roles.
• Proven experience leading end-to-end AI initiatives from experimentation through production deployment.
• Demonstrated ability to translate business challenges into scalable AI solutions.
• Experience mentoring Data Scientists and providing technical leadership across projects.

Machine Learning & Data Science Expertise

• Strong expertise in supervised and unsupervised learning techniques, including classification, clustering, anomaly detection, forecasting, and recommendation systems.
• Deep understanding of statistical modeling, hypothesis testing, experimental design, and data analysis.
• Experience developing, evaluating, and optimizing machine learning models in production environments.
• Strong knowledge of feature engineering, model explainability, model monitoring, and performance evaluation.
• Experience working with large-scale structured and unstructured datasets.

Generative AI & Agentic AI Expertise

• Hands-on experience building Generative AI solutions using Large Language Models (LLMs).
• Strong understanding of Retrieval-Augmented Generation (RAG), embeddings, vector databases, prompt engineering, and evaluation methodologies.
• Experience designing and implementing AI agents, multi-agent systems, and autonomous workflows.
• Experience integrating LLMs with enterprise systems, APIs, business applications, and knowledge repositories.
• Understanding of AI guardrails, responsible AI principles, hallucination mitigation, and AI governance requirements.
• Experience evaluating and selecting foundation models based on business, technical, and operational criteria.