Lead Data Scientist

We are seeking an experienced and highly motivated Lead Data Scientist to join our AI and Analytics team. This role will lead the design, development, and deployment of advanced Machine Learning and Generative AI solutions that address complex healthcare challenges. The ideal candidate will possess deep expertise in machine learning, statistical modelling, data science leadership, and production-scale AI systems. You will collaborate closely with Product Management, Engineering, Data Engineering, and Business stakeholders to drive innovation and deliver impactful AI-powered products.

Key Responsibilities:

Data Science and AI Leadership:

• Lead the development and deployment of advanced machine learning and AI solutions.
• Drive the technical direction and execution of Data Science initiatives across multiple projects.
• Mentor and guide Data Scientists, ML Engineers, and junior team members.
• Establish best practices for model development, validation, deployment, and monitoring.
• Partner with leadership to define the AI and analytics roadmap.

Machine Learning and Advanced Analytics:

• Design and build predictive, classification, recommendation, clustering, and anomaly detection models.
• Perform advanced statistical analysis and feature engineering on large-scale datasets.
• Develop scalable machine learning solutions that solve critical business problems.
• Evaluate and optimise model performance, accuracy, and reliability.

Generative AI and LLM Solutions:

• Design and implement Generative AI solutions using Large Language Models (LLMs).
• Build Retrieval-Augmented Generation (RAG) systems and AI-powered applications.
• Work with vector databases, embeddings, prompt engineering, and AI agent frameworks.
• Evaluate and improve the quality, safety, and performance of LLM-based systems.

Product and Stakeholder Collaboration:

• Collaborate with Product Managers, Engineering teams, and business stakeholders to identify high-impact AI opportunities.
• Translate business requirements into scalable AI and Data Science solutions.
• Present technical findings and recommendations to both technical and executive audiences.
• Drive adoption of AI-powered capabilities across products and platforms.

MLOps and Productionization:

• Lead model deployment, monitoring, and lifecycle management in production environments.
• Partner with Data Engineering and DevOps teams to establish MLOps best practices.
• Monitor model drift, performance degradation, and operational health metrics.
• Ensure scalability, reliability, and maintainability of AI systems.

Requirements:

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• 5+ years of experience in Data Science, Machine Learning, Artificial Intelligence, or Applied Research.
• Strong expertise in Python and SQL.
• Hands-on experience with machine learning frameworks such as Scikit-Learn, TensorFlow, PyTorch, XGBoost, or similar.
• Deep understanding of statistics, predictive modelling, experimentation, and data analysis.
• Proven experience building and deploying machine learning models into production.
• Experience working with large-scale structured and unstructured datasets.
• Strong problem-solving, communication, and leadership skills.

Preferred Qualifications

• Experience with Generative AI, Large Language Models (LLMs), and AI agent frameworks.
• Hands-on experience with LangChain, LangGraph, LlamaIndex, OpenAI APIs, Anthropic, or similar technologies.
• Experience building RAG-based solutions, vector search systems, and intelligent assistants.
• Familiarity with Databricks, Spark, Snowflake, and distributed data processing frameworks.
• Experience with AWS, Azure, or GCP cloud platforms.
• Healthcare, payer, provider, claims, or healthcare analytics experience preferred.
• Experience mentoring teams and leading cross-functional initiatives.
• Technical Skills: Python, SQL, Machine Learning, Deep Learning, Statistical Modelling, TensorFlow / PyTorch, Scikit-Learn, Generative AI and LLMs, LangChain / LangGraph, RAG Architectures, Vector Databases, Databricks / Spark, AWS / Azure / GCP, MLOps & Model Deployment.