Hybrid Life Sciences Systems Analyst

Verista; Cambridge (MA); Hybrid; USD 90,000 - 136,000; Full time

Job summary

Verista seeks a hands-on Business Analyst to bridge wet-lab scientists, computational biologists, and data platform engineers. You will translate complex biological workflows into clear specifications, structured user stories, and architectural diagrams.

Responsibilities include mapping data lifecycles, converting use cases into actionable items for platform and data engineering, and maintaining the team backlog while collaborating with the Platform Lead.

Qualifications

• Bachelor’s or master’s in bioinformatics, computational biology, life sciences, IT, or related field.
• 3-5+ years as a Business Analyst, Systems Analyst, or Technical Product Analyst in biotech/life sciences.
• Familiarity with multi-omics data modalities and bioinformatics background.
• Experience with Agile/Scrum using Jira and Confluence.
• Proficiency in workflow mapping tools (Visio, Lucid, Draw.io).
• Understanding of cloud-native data platforms (AWS, Databricks, Snowflake).
• Ability to translate between bench scientists and software engineering teams.
• Local Hybrid Requirement: on-site in Cambridge, MA 3 days/week.

Responsibilities

• Map end-to-end workflows and data lifecycles with cross-functional teams.
• Translate scientific use cases into epics and user stories for engineering teams.
• Maintain and groom team backlog and sprint goals with Platform Lead.
• Create user documentation and IT SOPs to drive platform adoption.

Skills

Biotech domain knowledge

Agile/Scrum delivery

Bench-to-IT translation

Education

BS/MS in Bioinformatics or related field

Tools

Jira

Confluence

Visio

Lucidchart

Draw.io

AWS

Databricks

Snowflake

Job description

Verista seeks a hands-on Business Analyst to bridge wet-lab scientists, computational biologists, and data platform engineers. You will translate complex biological workflows into clear specifications, structured user stories, and architectural diagrams.

Responsibilities include mapping data lifecycles, converting use cases into actionable items for platform and data engineering, and maintaining the team backlog while collaborating with the Platform Lead.