Data Scientist – AI/ML for Multimodal Systems Biology

Company Overview

Chronicle Bio is developing a data-driven healthcare platform that harnesses comprehensive biobanks to develop proprietary diagnostics and advanced AI to transform the management and treatment of chronic conditions globally. We integrate multimodal data (clinical records, biowearables, patient-reported outcomes, and multi-omic analysis) to generate insights that unlock new diagnostics, biomarkers, and therapeutic targets for complex neuroimmune conditions.

Role Summary

We are seeking a Data Scientist specializing in AI/ML model development to drive our next-generation discovery engine. This role is central to building and training novel algorithms that define disease endotypes and reveal actionable insights for diagnostics, biomarkers, and drug targets. The ideal candidate combines strong machine learning expertise with a background in systems biology or related disciplines. A background in immunology and wet-lab experience are an added advantage.

Key Responsibilities

Model Development & Deployment

• Design and implement cutting-edge machine learning and deep learning models to analyze large-scale, multimodal datasets (genomics, transcriptomics, epigenetics, proteomics, metabolomics, EHR, biowearables).
• Develop AI frameworks for patient stratification, disease endotyping, and target discovery.
• Contribute to building Chronicle Bio’s proprietary foundation model for neuroimmune conditions.

Scientific Insight & Creation

• Integrate multi-omic and clinical data to identify novel biomarkers, diagnostic signatures, and therapeutic targets.
• Collaborate with scientific leadership to translate findings into innovation and peer-reviewed publications.

Cross-Functional Collaboration

• Partner with wet-lab scientists, bioinformaticians, and clinical researchers to ensure data quality and biological relevance.
• Present complex analyses to scientific, operational, and investor audiences.
• Contribute to pipeline design, from experimental planning to computational interpretation.

Qualifications

Education: Ph.D. in Computational Biology, Systems Biology, Computer Science, or related field required.

Experience:

• Industry experience in biotech, pharma, or health-tech is strongly preferred.
• Demonstrated success applying AI/ML to biological or clinical datasets, ideally in immune or neuroimmune contexts.
• Experience with multi-omic integration and large-scale biological data (genomics, transcriptomics, proteomics, etc.).
• Familiarity with single-cell analysis, network biology, or spatial omics is a plus.

Technical Skills:

• Proficiency in Python and R, and deep learning frameworks such as PyTorch or TensorFlow.
• Strong understanding of statistical modeling, feature engineering, and model interpretability.
• Experience with cloud computing and high-performance data infrastructure.

Other Qualities:

• Strong scientific curiosity and ability to translate biological questions into computational strategies.
• Excellent communication skills to convey complex analyses across interdisciplinary teams.