At a Glance
- Tasks: Ensure the quality of complex biological datasets for groundbreaking AI projects.
- Company: Join a fast-growing start-up at the forefront of AI and biomedicine.
- Benefits: Competitive salary, equity options, flexible work arrangements, and growth opportunities.
- Other info: Collaborative culture focused on innovation and inclusivity.
- Why this job: Shape the future of biology and AI while working with top researchers.
- Qualifications: Expertise in omics data and strong analytical skills required.
The predicted salary is between 36000 - 60000 € per year.
Bioptimus is building the first universal AI foundation model for biology to fuel breakthrough discoveries and accelerate innovation in biomedicine. With more than $75M in funding, Bioptimus is a fast‑growing start‑up headquartered in Paris, incorporated in October 2023. Backed by leading international venture capitalists, our world‑class team of scientists and engineers is redefining the frontiers of AI and life sciences.
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 will 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 will work in collaboration with the R&D team and our engineers, using your skills to ensure our data meets the highest standards.
What you will be doing:
- 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.
What you will bring:
- 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.
How to stand out:
- 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).
The candidate journey:
- Screening: Once you have applied, the hiring team will review your application to determine if your work experience and skills align with the necessary proficiencies of this position.
- Technical Assessment: Given the technical nature of the role, you will be invited to complete a timed (1.5 hours) technical assessment on a dedicated platform. This stage consists of a short set of Python exercises designed to assess your coding proficiency.
- Interviews: Hiring Manager (30 min): A call with the Hiring Manager to discuss your background, motivation, and expertise. This will include a deep dive into your technical skills, including your experience with coding and cloud environments. Data Curation Project (Take‑home): The hiring team will have an introductory call with you to share expectations for the role and to provide you with a Data Curation assignment. This assignment covers one or more key data modalities and consists of the submission of Python code and a detailed report about your work. You will get to present your work to the team, and have an interview to explore your knowledge on data modalities and relevant technical artefacts. Team Fit (Series of 30‑min chats): You will meet with cross‑functional members of our team. This is a chance for us to assess your "community builder" mindset and for you to gauge the culture you will be supporting.
- Offer: Following the completion of the interviews, our hiring team will make a final decision and will be in touch to share the outcome of your interviews. If the team would like to move forward, the recruiter will discuss the details of our proposed offer with you.
- Onboarding: We are happy to have you joining the team. Once you have accepted and signed your offer, we will be in touch to begin the process of onboarding you to Bioptimus.
Why This is a Unique Opportunity:
- 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.
And benefit from:
- A collaborative and mission‑driven work environment.
- Competitive salary and equity package.
- Flexible work arrangements, including remote options.
- Opportunities for professional growth and leadership development.
- Shape the future of biology and AI by contributing to groundbreaking work.
We believe that the unique contributions of all Bioptimists create our success. To ensure that our culture continues to incorporate everyone’s perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, or disability status. Decisions related to hiring are made fairly, and we provide equal employment opportunities to all qualified candidates. We take responsibility for always striving to create an inclusive environment that makes every employee and candidate feel welcome.
Biology Data Quality Engineer employer: Bioptimus
Bioptimus is an exceptional employer, offering a dynamic and collaborative work environment in the heart of Paris, where innovation meets biomedicine. Employees benefit from competitive salaries, equity packages, and flexible work arrangements, alongside ample opportunities for professional growth and leadership development. Join a trailblazing team dedicated to redefining the future of AI in biology, while contributing to groundbreaking discoveries that have a meaningful impact on healthcare.
StudySmarter Expert Advice🤫
We think this is how you could land Biology Data Quality Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend relevant events, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for those interviews! Research Bioptimus and understand their mission in AI and biomedicine. Be ready to discuss how your skills in biology and data quality can contribute to their groundbreaking work.
✨Tip Number 3
Show off your projects! If you've worked on relevant data curation or machine learning projects, be sure to highlight them during interviews. Bring examples that demonstrate your expertise and problem-solving skills.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining the Bioptimus team.
We think you need these skills to ace Biology Data Quality Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Biology Data Quality Engineer role. Highlight your relevant experience in data quality management and any specific skills related to omics data. We want to see how your background aligns with our mission!
Showcase Your Skills:In your application, don’t just list your skills—show us how you've used them! Whether it's your programming prowess in Python or your analytical skills, give us examples that demonstrate your expertise in action.
Be Clear and Concise:When writing your application, clarity is key. Use straightforward language and avoid jargon where possible. We appreciate a well-structured application that makes it easy for us to see your qualifications at a glance.
Apply Through Our Website:We encourage you to apply directly through our website. This way, your application will be processed smoothly, and you'll be one step closer to joining our innovative team at Bioptimus!
How to prepare for a job interview at Bioptimus
✨Know Your Data Inside Out
Make sure you have a solid grasp of the different types of biological data, especially omics data. Brush up on your knowledge of transcriptomics, genomics, and proteomics, as well as their specific quality considerations. This will help you answer questions confidently and demonstrate your expertise.
✨Showcase Your Coding Skills
Since the role involves Python programming, practice coding exercises that focus on data validation and analysis. Familiarise yourself with libraries like matplotlib for data visualisation. Being able to discuss your coding experience and showcase relevant projects will set you apart from other candidates.
✨Prepare for Technical Assessments
Expect a technical assessment as part of the interview process. Review common data quality control procedures and be ready to implement them in Python. Practising timed coding challenges can help you manage your time effectively during the assessment.
✨Communicate Clearly and Effectively
Strong communication skills are crucial for this role. Be prepared to explain your data quality findings and recommendations to both technical and non-technical stakeholders. Practise articulating complex concepts in simple terms, as this will demonstrate your ability to collaborate with diverse teams.