Computational Biology & Genomics Analyst

Remote, USA
Posted Jun 13, 2026
Full-time

Job Title: Computational Biology & Genomics Analyst

Job Type: Contractor

Location: Remote

Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.

Key Responsibilities:
• Review and assess analytical rubrics for computational genomics projects, ensuring they meet scientific and operational standards.
• Evaluate sequence alignment strategies and assess the effectiveness of CRISPR design tools.
• Interpret and synthesize results from integrated sequence and experimental datasets.
• Collaborate with multidisciplinary teams to recommend data-driven improvements and best practices.
• Document findings and provide clear, actionable written feedback to stakeholders.
• Contribute to the development of guidelines and standards for sequence analysis and data integration.
• Stay abreast of advances in computational biology, genomics, and associated data technologies.

Required Skills and Qualifications:
• Expertise in sequence alignment techniques and familiarity with tools for CRISPR design.
• Proven experience interpreting large-scale genomics and experimental data.
• Strong proficiency with platforms that integrate sequence and experimental datasets.
• Exceptional written and verbal communication skills, with attention to clarity and detail.
• Demonstrated experience as a rubric reviewer or in a related quality assurance capacity.
• Ability to work independently in a remote, fast-paced environment.
• Advanced degree in computational biology, bioinformatics, genomics, or a related field.

Preferred Qualifications:
• Experience with designing or evaluating novel CRISPR-based experiments.
• Track record of contributing to bioinformatics pipeline development or standardization initiatives.
• Familiarity with regulatory or quality frameworks in genomics research.

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