Biology Data Quality Engineer

We are an AI-driven startup pioneering the development of a universal foundation model for biology. Our mission is to revolutionise biological research and innovation through cutting-edge technology. 

At Bioptimus, we are building cutting-edge AI models that require high-quality, diverse, and reliable biological data. We are looking for a meticulous and detail-oriented Biology Data Quality Engineer to ensure the integrity and usability of the various and complex datasets that are central to our mission.

In this critical role, you'll leverage your expertise in biology, data science, and machine learning to ensure the quality and consistency of biological data used to train and evaluate our foundation models. You'll work in collaboration with the R&D team and our engineers, using your skills to ensure our data meets the highest standards.

As a Biology Data Quality Engineer, you will own the following tasks:

• Data Validation Pipeline Development: Develop and implement comprehensive data validation protocols for diverse biological datasets (histology, omics, clinical). Ensure data integrity, consistency, and accuracy through rigorous quality checks. Design and implement automated data quality pipelines to streamline data validation and identify potential issues early in the data processing workflow.
• Data Curation & Standardization: Establish and enforce data standardization practices to facilitate seamless integration and analysis across different data types. Curate datasets to enhance their usability for machine learning.
• Collaboration & Communication: Work closely with the R&D team to understand data requirements and address data quality concerns. Communicate data quality findings and recommendations effectively to technical and non-technical stakeholders. Communicate and synchronize with external data providers.
• Documentation & Reporting: Maintain a detailed documentation of the data-quality assessment procedures, validation results, and data specifications. Generate regular reports on data quality metrics and trends.
• Data Source Evaluation: Evaluate and validate external public data sources, ensuring they meet our quality standards and are suitable for inclusion in our foundation model training.
• Continuous Improvement: Stay up-to-date with the latest data quality best practices and tools in the biological domain. Propose and implement improvements to our data- quality assessment processes and pipelines.

The successful candidate will have a ‘team-first’ kind of attitude; be independent, curious, and detail-oriented; thrive in a dynamic, fast-paced environment; and be fun to work with. We value individuals who bring strong domain expertise in biology alongside strong computational, hands-on skills.

• Omics Data Expertise. Deep understanding of transcriptomics data types (bulk, single-cell, spatial) and their specific quality considerations. Good knowledge of genomics and proteomics data.
• Data Quality Management: Proven experience in implementing data quality control procedures and pipelines. Familiarity with data validation tools and techniques.
• Analytical Skills: Strong analytical and problem-solving skills to identify and resolve data quality issues.
• Programming & Data Analysis: Proficiency in Python, good knowledge of data visualization libraries (e.g. matplotlib).
• Communication Skills: Excellent written and verbal communication skills to effectively convey data quality findings and recommendations.
• Educational Background: MSc in Biology, Computational Biology, Bioinformatics.

• Computational Pathology Data Expertise: Experience in machine learning analysis of histology images.
• Cloud expertise: Experience working with AWS.
• Data Annotation Experience: Experience with developing and implementing data annotation guidelines and processes. Experience with data ontologies.
• Proven experience building or contributing to large-scale data collections (e.g. Human Cell Atlas).
• Spatial alignment of multimodal datasets (e.g. alignment between different imaging modalities)

• Be part of a trailblazing team working at the intersection of AI, biotech, and biomedical research.
• Take on a high-impact leadership role, shaping the future of biomedical AI through strategic data partnerships.
• Work in a collaborative, innovation-driven environment with top researchers and industry experts.