Computational Biologist (AI/ML) - Essex Management
RemoteUnited States
Job Summary
Design and implement AI/ML models applied to genomic, proteomic, and multimodal clinical datasets to support precision oncology and translational research. Develop, test, and optimize pipelines for variant calling and annotation leveraging modern ML workflows, while curating and integrating genetic and clinical datasets into standardized, interoperable formats. Apply large language models, retrieval-augmented generation techniques, and graph neural networks to make biomedical data AI-ready. Generate high-quality, interpretable reports that translate complex outputs into actionable scientific and clinical insights for internal and external stakeholders. Own well-defined analytical tasks and small projects with minimal supervision, contributing substantively to team problem-solving and quality assurance activities.
Required Qualifications
- Working proficiency in the core technical tools, analytical approaches, and data standards relevant to the assigned program and department.
- Ability to work independently and within a team in a fast-paced, collaborative environment.
- Strong written and oral communication skills, including the ability to document work clearly and present findings to technical audiences.
- Proficiency with Microsoft Office applications.
- Proficiency in Python and SQL; demonstrated experience with ML frameworks (PyTorch, TensorFlow) and code versioning (Git).
- Strong background in machine learning and AI, including deep learning architectures (CNNs, GNNs) applied to biomedical or complex multi-modal datasets.
- Familiarity with genetic variant standards (HGVS, VCF) and clinical data ontologies.
- Demonstrated ability to work cross-functionally and communicate technical results clearly to diverse scientific and clinical audiences.
- Bachelor's degree or advanced degree in bioinformatics, computational biology, data science, genetics, biology, health informatics, clinical research, or a related field.
- 2 to 5 years of relevant professional or research experience.
- Demonstrated success applying AI/ML to biomedical or multi-modal datasets (genomics, proteomics, clinical).
Desired Qualifications
- Track record of publications or applied innovation in AI-driven data science for life sciences preferred.
- Experience applying LLMs or RAG approaches to scientific or clinical data problems preferred.
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