Staff Data Scientist

At IMO Health, a core team of software developers, data scientists, and domain experts combine computer science, healthcare, and life sciences expertise to help professionals access high-quality health information quickly and easily. We are seeking a Staff Data Scientist with deep expertise in 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 with cross-functional teams to 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, and MLOps best practices to deliver robust, client-facing solutions in the life sciences domain.Join our growing Data Science & Analytics department as a Staff Data Scientist to drive AI-powered innovation in healthcare and life sciences! WHAT YOU'LL DO: 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 with cross-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 and maintain scalable data and AI pipelines, integrating diverse data sources including structured, unstructured, 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 and MLOps best practices, including bolthires/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.WHAT YOU'LL NEED: PhD preferred (or Master’s with 8+ years of experience) in Biostatistics, Bioinformatics, Computer Science, Health Informatics, or a 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 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.Understanding of data privacy, ethics, and regulatory requirements in healthcare (e.g., HIPAA). Proven ability to collaborate cross-functionally with engineers, scientists, domain experts and clients. Excellent communication, documentation, and presentation skills; demonstrated experience mentoring colleagues and contributing to scientific publications or conference presentations in the life sciences domain. Curious, innovative, and solution-oriented mindset with a passion for building tools that advance evidence generation and decision-making in life sciences.