Product Development

Position Overview

We are seeking an expert-level Lead Data Scientist to drive enterprise AI/ML, GenAI, LLM Ops, Agentic AI, and autonomous agent initiatives for our US Healthcare Revenue Cycle Management (RCM) platform. This Individual Contributor (IC) role focuses on hands-on technical leadership, building scalable, production-ready AI/ML models, autonomous agents, and predictive/analytical pipelines across RCM modules such as Claims, Prior Authorization, Coding, Collections, Scheduling, and EDI. The role blends deep AI/ML expertise with data strategy, cloud-native deployment, RPA integration, and compliance governance, enabling intelligent automation and data-driven decision-making at scale.

Job Roles & Responsibilities

AI/ML & Agentic AI Development:

• Lead design, development, and deployment of advanced ML/DL models, LLM-based solutions, and Agentic AI workflows.
• Build autonomous agents, multi-agent orchestration pipelines, and GenAI applications for RCM operations.
• Conduct feature engineering, model validation, hyperparameter tuning, and optimization for production-level performance.
• Integrate AI models with RPA, microservices, APIs, and cloud pipelines for automation.

Data Engineering & Pipeline Oversight:

• Collaborate with data/ETL engineers to design scalable batch and real-time pipelines for training and production.
• Ensure data quality, preprocessing, lineage, and regulatory compliance (HIPAA, SOC 2, GDPR).
• Optimize data pipelines feeding AI/ML models, autonomous agents, and analytics dashboards.

MLOps / LLM Ops

• Implement CI/CD pipelines, model versioning, monitoring, rollout/rollback strategies, and safe deployment for autonomous agents.
• Collaborate with cloud teams to build scalable AI/ML infrastructure on AWS SageMaker/Bedrock, Azure ML, or GCP AI.
• Maintain audit logs, governance frameworks, and performance monitoring for LLMs, GenAI, and agentic workflows.

Analytics & Insights:

• Develop predictive, prescriptive, and generative analytics for RCM processes.
• Design and maintain dashboards, KPIs, and reports for operational, compliance, and business teams.
• Provide actionable insights enabling AI-driven automation and improved operational efficiency.

Collaboration & Mentorship

• Serve as technical IC mentor to junior data scientists and analysts.
• Collaborate with AI/ML engineers, RPA developers, DevOps, product, and business teams on cross-functional initiatives.
• Establish and enforce best practices for model development, deployment, and agentic AI workflows.

Candidate Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or related fields.
• 8–12+ years of experience in data science, AI/ML, GenAI, LLM Ops, and healthcare analytics.
• Proven expertise in Agentic AI, multi-agent orchestration, and autonomous agent development.
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and production deployment of AI models.
• Strong knowledge of HIPAA, SOC 2, GDPR, and compliance frameworks in healthcare..

Technical Expertise:

• Programming: Python, R, SQL, Scala
• AI/ML Frameworks: TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers
• GenAI / LLM Ops / Agentic AI Platforms
• Data Engineering: Spark, Hadoop, Snowflake, Redshift, BigQuery, Airflow, dbt
• Cloud Platforms: AWS (SageMaker, Bedrock), Azure ML, GCP AI/ML
• RPA Integration: UiPath, Automation Anywhere, Blue Prism
• CI/CD & DevOps: Docker, Kubernetes, MLOps/LLMOps pipelines
• Analytics & Visualization: Tableau, Power BI, Looker (preferred)

Skillset:

• Strong analytical, problem-solving, and modeling skills for large-scale healthcare datasets.
• Expertise in autonomous AI agents, GenAI, multi-agent orchestration, and RCM automation.
• Ability to deliver production-ready, compliant, and scalable AI/ML solutions independently.
• Excellent collaboration, communication, and technical mentorship skills.
• Detail-oriented, compliance-first mindset, and strategic thinking.

Strategic Impact:

• Deliver production-ready AI/ML models, autonomous agents, and predictive analytics across RCM modules.
• Enable data-driven decision-making, AI automation, and operational excellence.
• Ensure scalable, compliant, and high-performance AI systems within the organization.
• Serve as the technical authority for AI/ML, GenAI, LLM Ops, and Agentic AI adoption in healthcare RCM.

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