Data Scientist, Clinical Decision Support- LLMs

About the Team

The Clinical Decision Support (CDS) team at Abiomed develops advanced hemodynamic and predictive solutions for clinicians caring for critically ill patients supported by Abiomed devices. The team’s mission is to deliver reliable, actionable insights embedded directly into clinical workflows, enabling physicians to make more informed treatment decisions.

Our work focuses on building Software as a Medical Device (SaMD) solutions, beginning with clinical algorithm design and validation, followed by software integration and deployment into clinician-facing platforms.

We develop signal processing and machine learning–driven CDS products such as early detection models, hemodynamic monitoring tools, and decision support systems by leveraging high frequency Impella waveform data, EMRs, clinical trial registries, and advanced Generative AI / LLM techniques.

Position Overview

We are seeking an Experienced Data Scientist to contribute to the design, development, and validation of next-generation clinical decision support solutions for patients supported by Abiomed devices. This role spans machine learning model development using high-frequency physiologic and clinical data, LLM-driven extraction of insights from unstructured EMR sources, and deployment of AI-enabled solutions in regulated Software as a Medical Device (SaMD) environment. The ideal candidate will combine strong technical depth in AI/ML with the ability to translate complex clinical data into reliable, actionable tools that support clinician decision-making.

Key Responsibilities

• Develop advanced prompt engineering strategies and implement evaluation frameworks to optimize accuracy, reducing hallucinations and safety guardrails.
• Support design and development of secure, scalable cloud infrastructure (IaaS/PaaS) for hosting LLMs, including Azure ML Service, Azure Kubernetes Service (AKS), and Container Apps
• Configure and optimize data indexing for Retrieval-Augmented Generation (RAG) techniques using Azure AI Search or vector databases
• Support and maintain documentation for AI models, ensuring validation, transparency, explainability, and traceability for regulatory submissions.
• Develop and implement machine learning models for clinical decision support, translating time-series physiologic signals and clinical data into robust, actionable insights for patient management
• Collaborate cross-functionally with data scientists, ML and software engineers, and quality teams to advance CDS solutions from concept through deployment.

Required Qualifications

• Master’s degree with 2+ years of experience or PhD in a relevant field.
• Hands-on experience with LLMs/GenAI within the cloud ecosystem (Azure preferred)
• Proficiency in Python for scripting and integrating LLM/agentic frameworks (e.g., LangChain)
• Understanding of RAG Architecture, vector databases, embedding models, and search technologies
• Experience with SQL databases and APIs for ETL Operations
• Experience with machine learning model development and frameworks (PyTorch, TensorFlow, Scikit-learn)
• Background in signal processing and biostatistics to analyze physiologic data and apply rigorous methods to model development and validation.
• Working knowledge of bio-statistics

Preferred Qualifications

• Knowledge of cardiovascular physiology or critical care/ICU workflows
• Experience with clinical datasets and medical device data
• Understanding of Software as a Medical Device (SaMD) development lifecycle and FDA regulatory expectations
• Experience deploying models into production systems
• Familiarity with multi-modal datatypes like medical images, sensor-based data etc.

What Makes This Role Unique

• Apply cutting-edge Generative AI and LLM techniques to real-world clinical problems
• End-to-end ownership of development, from design through deployment. Direct impact on life-saving technologies that support critical care ICU patients
• Work with rich clinical data, high-frequency physiologic signals, and other real-world sources, to develop robust and clinically relevant solutions