Bioinformatics / Omics Data Expert

As a Scientific Data Engineer in OncDS, you will help build reliable, scalable oncology reference datasets and modernize data curation workflows. You will collaborate with cross-functional partners to implement automation and AI-enabled improvements that keep data current, accurate, and fit for downstream science. Your work enables oncology drug discovery and clinical development by supporting high-quality, reproducible data assets. This role offers growth at the intersection of data engineering, computational biology, and oncology research, with a strong emphasis on operational excellence and impact.

• salary range $103,600 – $192,400
• performance-based cash incentive
• potential eligibility for annual equity awards
• comprehensive benefits package (health, life, disability)
• 401(k) with company contribution and match
• generous time off (vacation, personal days, holidays)

• Identify opportunities to simplify and automate data processes and modernize data curation workflows
• Develop and improve automated data processing, quality control, and dataset update workflows
• Curate and maintain key oncology reference datasets with emphasis on quality, reproducibility, and reliability
• Manage reference dataset lifecycle including documentation, versioning, and traceable updates
• Assess data structure and quality for downstream use, addressing identifiers, keys, missing values, and integrity issues with partners
• Contribute to best practices for reproducible workflows, data handling, and scalable dataset operations
• Keep oncology reference datasets current and aligned with evolving scientific and organizational priorities

• Degree in bioinformatics, computational biology, data science, computer science, or related field, or equivalent experience
• Experience with biological, experimental, or omics datasets in a research environment
• Proficiency in Python or R
• Experience with data wrangling, quality control, and reproducible analysis workflows
• Familiarity with workflow automation and scripting for recurring tasks
• Familiarity with database principles, data modeling, normalization, primary keys, joins, and handling missing values
• Knowledge of version control and Unix/Linux environments
• Strong organizational skills, attention to detail, and drive for operational excellence
• Ability to learn quickly, manage multiple tasks, and collaborate effectively
• Excellent written and verbal communication across technical and scientific teams

• curiosity
• attention to detail
• collaboration
• Python
• R Programming
• data wrangling