Senior Scientist - AI/ML Drug Discovery
Overview
We are seeking a highly motivated Senior Scientist to join a cutting-edge Research and Development team applying advanced AI/ML approaches to accelerate the discovery of novel drug targets, biomarkers, and therapeutic candidates. This role focuses on developing and scaling simulation and modeling platforms that integrate multi-omic datasets, causal inference, and deep learning to reveal hidden biological mechanisms of disease. The ultimate goal: to advance therapies across areas such as neurodegeneration, oncology, immunology, and beyond.
Position Summary
The Senior Scientist will play a key role in evolving and applying an AI-driven simulation platform to build in silico patient models and accelerate drug discovery. You will work closely with engineering, computational biology, and precision medicine teams to design, implement, and refine AI tools that integrate multi-omic data, automate feature extraction, and generate actionable biological insights. This position offers the opportunity to influence platform strategy, contribute to high-impact collaborations, and present findings at scientific conferences and in peer-reviewed publications.
Key Responsibilities
Lead efforts to integrate deep learning, large language models (LLMs), and causal inference into data pipelines and biological modeling workflows.
Automate biological knowledge extraction and target identification from AI-driven disease models.
Collaborate with cross-functional teams to design novel in silico patient models and explore new applications in therapeutic discovery.
Mine and analyze multi-modal datasets (public and proprietary) for new insights into disease biology.
Clearly communicate results to internal teams, external collaborators, and biopharma partners.
Contribute to scientific publications, conference presentations, and strategic platform development.
Qualifications
Advanced degree (PhD, MD, or equivalent) in a quantitative discipline or life sciences field.
Proven experience with advanced ML techniques, Bayesian methods, or causal inference approaches.
Proficiency in statistical programming (Python, R/Bioconductor) and familiarity with ML frameworks such as PyTorch or TensorFlow.
Experience with multi-omics, single-cell, or high-throughput screening datasets.
Background in automating AI/ML workflows and integrating heterogeneous biological datasets.
Strong communication skills and ability to collaborate effectively with computational and experimental scientists.
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