Data4Life
Data Engineer(m/w/x)
Full-timeWith HomeofficeExperienced
Berlin, Potsdam
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BIBioptimus
Biology Data Quality Engineer(m/w/x)
Berlin
Full-timeWith Home OfficeExperienced
AI/ML
Data Science
Nejo AI Summary
Developing data validation protocols for histology, omics, and clinical datasets for an AI biology foundation model. Strong domain expertise in biology required. Equity package included.
Requirements
- Team-first attitude and independence
- Curiosity and detail-orientation
- Ability to thrive in dynamic environments
- Strong domain expertise in biology
- Strong computational and hands-on skills
- Deep understanding of transcriptomics data
- Knowledge of genomics and proteomics data
- Experience in data quality control
- Familiarity with data validation tools
- Strong analytical and problem-solving skills
- Proficiency in Python
- Knowledge of data visualization libraries
- Excellent written and verbal communication
- MSc in Biology, Computational Biology, or Bioinformatics
- Experience in machine learning for histology
- Experience working with AWS
- Experience with data annotation guidelines
- Experience with data ontologies
- Experience building large-scale data collections
- Experience in spatial alignment of datasets
Tasks
- Develop comprehensive data validation protocols for histology, omics, and clinical datasets
- Design automated pipelines to streamline data validation and identify issues early
- Establish data standardization practices for seamless integration across different data types
- Curate biological datasets to enhance their usability for machine learning
- Collaborate with the R&D team to address specific data quality requirements
- Communicate findings and recommendations to technical and non-technical stakeholders
- Coordinate and synchronize data standards with external providers
- Document quality assessment procedures, validation results, and data specifications
- Generate regular reports on data quality metrics and trends
- Evaluate external public data sources for foundation model training suitability
- Implement improvements to data quality processes based on industry best practices
Work Experience
- approx. 1 - 4 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- matplotlib
- AWS
Benefits
Flexible Working
- Flexible work arrangements
- Remote work options
Competitive Pay
- Competitive salary
- Equity package
Career Advancement
- Professional growth opportunities
- Leadership development opportunities
Purpose-Driven Work
- Collaborative and mission-driven environment
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Bioptimus and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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BIBioptimus
Biology Data Quality Engineer(m/w/x)
Berlin
Full-timeWith Home OfficeExperienced
AI/ML
Data Science
Nejo AI Summary
Developing data validation protocols for histology, omics, and clinical datasets for an AI biology foundation model. Strong domain expertise in biology required. Equity package included.
Requirements
- Team-first attitude and independence
- Curiosity and detail-orientation
- Ability to thrive in dynamic environments
- Strong domain expertise in biology
- Strong computational and hands-on skills
- Deep understanding of transcriptomics data
- Knowledge of genomics and proteomics data
- Experience in data quality control
- Familiarity with data validation tools
- Strong analytical and problem-solving skills
- Proficiency in Python
- Knowledge of data visualization libraries
- Excellent written and verbal communication
- MSc in Biology, Computational Biology, or Bioinformatics
- Experience in machine learning for histology
- Experience working with AWS
- Experience with data annotation guidelines
- Experience with data ontologies
- Experience building large-scale data collections
- Experience in spatial alignment of datasets
Tasks
- Develop comprehensive data validation protocols for histology, omics, and clinical datasets
- Design automated pipelines to streamline data validation and identify issues early
- Establish data standardization practices for seamless integration across different data types
- Curate biological datasets to enhance their usability for machine learning
- Collaborate with the R&D team to address specific data quality requirements
- Communicate findings and recommendations to technical and non-technical stakeholders
- Coordinate and synchronize data standards with external providers
- Document quality assessment procedures, validation results, and data specifications
- Generate regular reports on data quality metrics and trends
- Evaluate external public data sources for foundation model training suitability
- Implement improvements to data quality processes based on industry best practices
Work Experience
- approx. 1 - 4 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- matplotlib
- AWS
Benefits
Flexible Working
- Flexible work arrangements
- Remote work options
Competitive Pay
- Competitive salary
- Equity package
Career Advancement
- Professional growth opportunities
- Leadership development opportunities
Purpose-Driven Work
- Collaborative and mission-driven environment
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Bioptimus and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
About the Company
Bioptimus
Industry
Research
Description
The company is building the first universal AI foundation model for biology to fuel breakthrough discoveries and accelerate innovation in biomedicine.
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