Lead Data Scientist

Caring. Connecting. Growing together.

We are seeking an accomplished Lead Data Scientist with proven expertise in advanced AI/ML, Generative AI, and healthcare or health insurance analytics. The ideal candidate will drive innovation and strategic value by leading teams to deliver scalable, secure, and responsible AI solutions for healthcare data. This role requires hands-on technical leadership, deep domain knowledge, and the ability to influence business outcomes across the organization.

Primary Responsibilities:

• Leadership & Strategy
  • Lead and mentor a team of data scientists and AI engineers, fostering a culture of innovation, collaboration, and continuous learning
  • Define and drive the AI/ML roadmap for healthcare analytics, aligning with organizational goals and regulatory requirements
  • Partner with business stakeholders, product managers, and technology leaders to translate complex healthcare challenges into actionable AI solutions
  • Oversee project delivery, and performance management for the data science team
• Healthcare/Health Insurance Domain Expertise
  • Apply deep understanding of healthcare and health insurance data, including claims, clinical, provider, and member datasets
  • Ensure compliance with healthcare regulations (HIPAA, HITECH, etc.) and data governance standards
• Advanced AI/ML & GenAI Engineering
  • Architect and implement machine learning, deep learning, and generative AI solutions using frameworks such as LangChain, LangGraph, HuggingFace, and LLMs (GPT, Gemini, LLaMA)
  • Build and optimize Retrieval-Augmented Generation (RAG) pipelines, multi-agentic workflows, and prompt-based learning systems for healthcare use cases
  • Lead the adoption of Responsible AI principles - fairness, transparency, explainability, and security - in all model development and deployment
• Cloud-Native AI Development & MLOps
  • Design and manage scalable AI/ML solutions on cloud platforms (Azure, AWS, GCP), leveraging services like Databricks, ML Studio, Bedrock, SageMaker, Vertex AI
  • Integrate models with enterprise systems using REST APIs, SDKs, and orchestration tools
  • Oversee CI/CD pipelines, model monitoring, drift analysis, and containerization (Docker, MLFlow)
• Collaboration & Stakeholder Management
  • Engage with cross-functional teams to deliver business impact through data-driven insights and AI-powered products
  • Manage vendor relationships and external data partnerships
  • Champion best practices in agile development, code management (GitHub), and documentation
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• 7+ years of experience in data science, AI, and ML-Ops engineering roles, with 3+ years in healthcare or health insurance analytics
• Hands-on experience with GenAI frameworks (LangChain, HuggingFace, LLMs) and cloud-native AI services (Azure, AWS, GCP)
• Experience with Health Insurance Analytics and deep domain expertise
• Demonstrated leadership experience managing data science or AI teams
• Deep expertise in Python, PySpark, SQL, and big data technologies (Hadoop, Spark)
• Solid understanding of healthcare data, regulatory compliance, and domain-specific modeling
• Proficiency in MLOps, CI/CD, GitHub, and agile methodologies
• Proven excellent communication, stakeholder management, and problem-solving skills

Preferred Qualifications:

• Knowledge of advanced deep learning architectures (CNNs, Autoencoders) and NLP (Transformers, RNNs)
• Familiarity with healthcare interoperability standards (HL7, FHIR)
• Exposure to reporting tools and dashboard creation

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