Staff Data Scientist, Life Sciences AI (RWE & Meta-Analysis)

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 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. 

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.