Lead Data Scientist

Responsibilities:

• AI/ML Model Development: Lead the design, development, training, and deployment of advanced machine learning, deep learning, NLP, and GenAI models for healthcare applications.
• Data Strategy and Engineering: Build effective data strategies to extract, clean, structure, label, and process large-scale healthcare datasets to improve model performance.
• Healthcare Data Intelligence: Work with complex clinical data, medical records, historical coding decisions, claim outcomes, and denial-related datasets to identify meaningful patterns.
• LLM Development: Develop and optimise LLM-based solutions for understanding clinical documentation, medical coding automation, and intelligent decision-making.
• Model Training and Optimisation: Improve model accuracy through data curation, feature engineering, fine-tuning, training strategies, and continuous performance evaluation.
• Data Mining and Pattern Recognition: Identify rare but valuable patterns across large datasets, complex classification systems, and high-cardinality label spaces.
• AI Architecture and Scalability: Design scalable AI/ML architectures covering real-time inference, batch processing, model training pipelines, and production deployment.
• Continuous Learning: Build feedback loops using historical decisions, human feedback, and real-world outcomes to continuously improve AI model performance.
• Team Leadership: Mentor data scientists and ML engineers, establish best practices, and collaborate with engineering and product teams to deliver AI solutions.
• Model Monitoring and Governance: Establish model evaluation, monitoring, versioning, explainability, and deployment practices to ensure reliable and compliant AI systems.

Requirements:

• The focus is on 6+ years Core Machine Learning and Data Science professionals who have hands-on experience in - Developing machine learning and deep learning models from scratch, Training, testing, validating and optimising ML models, working on model development, Taking ownership of the complete model development lifecycle.
• Strong expertise in Python and frameworks such as PyTorch or TensorFlow.
• Experience building and deploying production-grade AI/ML models.
• Strong understanding of LLMs, fine-tuning, model training, and optimisation.
• Experience working with large-scale, complex, unstructured datasets.
• Strong expertise in data mining, data labelling, data curation, and feature engineering.
• Experience designing scalable AI/ML pipelines and architectures.
• Solid understanding of NLP fundamentals and Transformer architectures.
• Fine-tuning, model building and model development experience.