IMO Health is looking to develop innovative tools that empower clients to conduct advanced evidence synthesis and analysis by leveraging AI/LLM technologies and real-world evidence generation.
Requirements
Deep understanding of statistical modeling, meta-analysis methodology, and evidence synthesis techniques relevant to post-market and real-world evidence (RWE) applications.
Strong background in NLP, and AI principles, with a focus on LLMs, prompt engineering, and information extraction from scientific text.
Proficiency in Python and major ML/NLP frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, and scikit-learn.
Demonstrated experience designing and deploying AI-driven analytical tools or platforms, including LLM-based workflows for literature mining, evidence synthesis, or clinical data interpretation.
Familiarity with vector databases (e.g., Pinecone, PostgreSQL) and knowledge graph or semantic data modeling for organizing biomedical and clinical information.
Strong grasp of experimental design, statistical inference, and validation methods to ensure scientific rigor in software outputs.
Experience in biomedical or healthcare data analysis, including integration of real-world data sources such as literatures, EHR, claims, or registry data.
Responsibilities
Design and develop AI-driven software tools that enable clients to perform meta-analysis, systematic reviews, and real-world evidence (RWE) generation efficiently and accurately.
Translate complex statistical and meta-analytic workflows into scalable, automated product features and user-facing applications.
Leverage LLMs, prompt engineering, and information extraction techniques to automate literature review, data curation, and evidence synthesis from clinical and real-world data sources.
Build and maintain scalable data and AI pipelines, integrating diverse data sources including structured, unstructured, and real-world datasets.
Implement modern software engineering and MLOps best practices, including CI/CD, testing, monitoring, and version control, to support scalable and reliable deployments.
Evaluate emerging AI/ML and statistical technologies, drive proof-of-concept initiatives, and shape the technical roadmap for evidence-generation tools.
Mentor and support team members, fostering skill development in NLP, LLMs, and statistical modeling for healthcare applications.
Other
Collaborate with cross-functional teams to ensure scientific rigor, methodological validity, and usability of developed solutions.
Ensure compliance with data privacy, security, and regulatory standards across all data handling and model deployment activities.
Interpret and communicate insights effectively to both technical and non-technical stakeholders through visualizations, reports, and presentations.
Champion a culture of scientific and technical excellence, continuous learning, and innovation in applying AI to healthcare and life sciences challenges.
Proven ability to collaborate cross-functionally with engineers, scientists, domain experts and clients.
