Data Scientist, Life Sciences AI; RWE & Meta-Analysis

At IMO Health, a core team of software developers, data scientists, and domain experts combine computer science, healthcare, and life sciencesexpertiseto help professionals access high-quality health information quickly and easily.

We are seeking a Staff Data Scientist with deepexpertisein statistical modeling, meta-analysis methodologies, AI/LLM technologies, and real-world evidence (RWE) generation to help design and develop innovative tools that empower clients to conduct advanced evidence synthesis and analysis.

This role sits at the intersection of science, technology, and client delivery-translating complex analytical workflows into intelligent, scalable software solutions. You will collaborate withcross-functional teamsto design, build, and optimize

AI-enabled tools for meta-analysis. The ideal candidate will bring strong scientific understanding, product-thinking mindset, and experience implementing cloud-based AI infrastructure, scalable ML pipelines, andMLOpsbest practices to deliver robust, client-facing solutions in the life sciences domain.

Join our growing

Data Science & Analyticsdepartmentas a Staff Data Scientist to drive AI-powered innovation in healthcare and life sciences!

• 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.
• Collaborate withcross-functional teams to ensure scientific rigor, methodological validity, and usability of developed solutions.
• 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 andmaintainscalable data and AI pipelines, integrating diverse data sources including structured, un, and real-world datasets.
• Ensure compliance with data privacy, security, and regulatory standards across all data handling and model deployment activities.
• Implement modern software engineering andMLOpsbest 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.
• Interpret and communicate insights effectively to both technical and non-technical stakeholders through visualizations, reports, and presentations.
• Mentor and support team members, fostering skill development in NLP, LLMs, and statistical modeling for healthcare applications.
• Champion a culture of scientific and technical excellence, continuous learning, and innovation in applying AI to healthcare and life sciences challenges.

• PhD preferred (or Master’swith8+ years of experience) in Biostatistics, Bioinformatics, Computer Science, Health Informatics, ora related field.
• 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 asPyTorch, Tensor Flow, Hugging Face Transformers,Lang Chain, 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, Postgre
  
  SQL) 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 asliteratures,EHR, claims, or registry data.
• Understanding ofdata privacy, ethics, and regulatory requirements in healthcare…