Computational Biologist (AI/ML) - Essex Management (Contingent Roles)

Responsibilities

• Own and execute well-defined analytical and technical tasks and small projects with minimal supervision from senior staff or your manager.
• Assist Associate Informaticists with task-related questions, troubleshooting, and orientation.
• Contribute substantively to team discussions on methodology, standards, tools, and technical decisions.
• Apply working familiarity with best practices in your domain to deliver scientifically sound, high-quality work products.
• Support peer review of deliverables and contribute to quality assurance activities.
• Contribute to team documentation, SOPs, and knowledge base materials.
• Shadow senior staff during candidate interviews to begin developing evaluation skills.
• Participate in internal training sessions, brown bags, and knowledge-sharing activities.
• Perform other related duties as assigned.

Qualifications

• Required skills:
  • 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.
• Role-Specific Skills:
  • 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.
• Required Experience:
  • 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.
• Role-Specific Experience:
  • Demonstrated success applying AI/ML to biomedical or multi-modal datasets (genomics, proteomics, clinical).
  • 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.