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14 days ago
Requirements and responsibilities
We aren’t just building chatbots. We are architecting autonomous AI that transforms patient care.If you are a Data Scientist who is tired of leaving your models dying in Jupyter Notebooks and you want to build cutting-edge, production-ready systems, this is your next playground.You will bridge the gap between heavy statistical data science and cutting-edge GenAI. You will design, build, and deploy multi-agent systems and scalable ML pipelines to optimize how we map complex patient journeys and drive commercial innovation.The MissionYou will bridge the gap between heavy statistical data science and cutting-edge GenAI.You will design, build, and deploy multi-agent systems and scalable ML pipelines to optimize how we map complex patient journeys and drive commercial innovation.The Tech Ecosystem You’ll Master:Agentic AI & GenAI: LangChain, AutoGen, MCP (Model Context Protocol), A2A systems, RAG architecture, and Vector Databases.Core Analytics: Next Best Action (NBA) engines, Multi-Touch Attribution (MTA), Predictive Patient Event Modeling (line switches, initiation).ML Engineering & MLOps: Databricks, AWS SageMaker, Docker, CI/CD pipelines, and end-to-end model deployment monitoring.Data Ecosystem: Large-scale healthcare datasets (Claims, EHR/EMR, CRM like Veeva) under strict HIPAA compliance.Who You Are:You hold a Master’s (5-7+ years exp) or PhD (3-5+ years exp) in Data Science, CS, Stats, or a related quantitative field.You have a deep background in Pharma Commercial Analytics or Healthcare Consulting.You are a storyteller who can look at complex algorithmic outputs and explain the business impact clearly to senior global stakeholders.What Success Looks Like:Deploying production-grade ML pipelines and GenAI decision agents that enable real-time, smarter decision-making for commercial sales and marketing teams globally.
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