Lead Data Scientist

Role Overview

• Lead the end-to-end lifecycle of Generative AI and agentic AI solutions, including ideation, research, prototyping, implementation, evaluation, deployment, and production support
• Architect and develop scalable GenAI systems such as LLM-based applications, Retrieval-Augmented Generation (RAG), AI agents, and intelligent automation workflows to improve decision-making, operational efficiency, and customer outcomes
• Drive adoption of best practices in prompt engineering, LLM evaluation, fine-tuning strategies, and secure model hosting where applicable
• Apply advanced data science and machine learning techniques across a wide range of use cases including predictive modeling, forecasting, classification, anomaly detection, recommendation systems, NLP, and GenAI-enabled analytics
• Develop custom ML models and analytical frameworks tailored to complex healthcare and enterprise data challenges
• Perform exploratory data analysis (EDA), feature engineering, and statistical analysis to generate actionable insights and guide solution design
• Collaborate with engineering and platform teams to deploy, monitor, and maintain ML and GenAI solutions in production environments using cloud-native and MLOps best practices
• Establish model performance tracking, drift detection, reliability monitoring, and continuous improvement processes for deployed models and AI agents
• Ensure solutions are scalable, cost-efficient, resilient, and aligned with enterprise architecture standards
• Ensure strong model governance, documentation, and auditability across all ML and GenAI solutions
• Apply Responsible AI principles including explainability, transparency, data privacy, security, and regulatory compliance, particularly within healthcare contexts
• Provide guidance on safe, compliant, and ethical use of LLMs and agentic AI across enterprise use cases
• Work closely with business stakeholders and product managers to translate requirements into clear analytical problem statements, solution designs, and execution roadmaps
• Present technical solutions, insights, and progress updates to both technical and non-technical audiences, including senior leadership
• Mentor and guide junior data scientists and engineers, fostering a culture of technical excellence, innovation, and continuous learning.

Requirements

• Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Data Science, or a related quantitative field
• 7–10+ years of experience in data science or advanced analytics, with significant hands-on experience in Generative AI and LLM-based systems
• Proven experience designing, building, and deploying production-grade ML and GenAI solutions, including LLMs, RAG architectures, and AI-driven automation
• Strong proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, and/or Hugging Face
• Deep understanding of machine learning algorithms, statistical modeling, experimental design, and data structures
• Experience working with cloud platforms (Azure preferred; AWS/GCP acceptable)
• Ability to clearly communicate complex technical concepts to diverse business and technical audiences
• Demonstrated leadership in driving projects, influencing stakeholders, and mentoring team members.

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Python
• PyTorch
• Tensorflow

Benefits

• Competitive compensation package
• Health insurance
• 401(k) matching
• Flexible work hours
• Professional development opportunities