Bioptimus
Clinical Data Manager (Senior)
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Clinical Data Manager (Senior)
Clinical Data Manager (Senior) – Bioptimus
Bioptimus is building the first universal AI foundation model for biology to fuel breakthrough discoveries and accelerate innovation in biomedicine. With over $75M in funding, Bioptimus is a fast-growing startup headquartered in Paris, founded in October 2023. Backed by leading international venture capitalists, our team of world-class scientists and engineers is redefining the intersection of AI and life sciences.
This is a remote role. Our headquarters are in Paris, but the position can be performed remotely outside the region.
About the Role
We’re looking for a technical, execution-focused Clinical Data Manager to bridge the gap between unstructured, real-world data and our frontier AI models. In this role, you will serve as the authority on clinical data structures, acting as the technical liaison during interactions with global partners to standardise and harmonise data pipelines.
You’ll operate within our STELA program, structuring clinical datasets. This hands-on role involves:
- Writing reproducible code
- Enforcing data quality controls
- Designing data dictionaries and ontologies for our models
About the STELA Program
We recently launched the Spatial Tissue Embedding Learning Atlas (STELA), a multinational spatial data initiative. Collaborating with 10x Genomics and Broad Clinical Labs, STELA serves as the foundation for M-Optimus, aiming to profile up to 100,000 patient specimens across three continents (US, Europe, and Asia). This initiative integrates:
- High-resolution spatial transcriptomics
- Histopathology imaging
- Longitudinal clinical records
STELA fuels AI innovation and precision medicine.
What You’ll Be Doing
As our Clinical Data Manager, you’ll operate at the intersection of data engineering, clinical science, and partner collaboration, focusing on two key domains:
Partner Data Engineering & Collaboration
- Technical Partner Interface: Engage in detailed conversations with external partners (hospitals, research institutions, CROs/CMOs), analysing diverse clinical data structures and workflows.
- Order from Uncertainty: Translate ambiguous source data into harmonised, AI-ready assets.
- Ontology Integration: Map clinical data to industry-standard biomedical ontologies (e.g., SNOMED, ICD) with a focus on oncology and immunology.
Data Governance, Quality, and Automation
- Data Dictionary Architecture: Design and maintain data dictionaries, schemas, and metadata models compatible with the STELA pipeline, ensuring integration with existing systems.
- Enforcing Ingest Quality: Develop and automate data quality control (QC) and validation frameworks to ensure incoming data meets integrity, completeness, and programmatic consistency standards.
- Reproducible Pipeline Code: Write production-grade Python code for data cleaning, validation, and harmonisation.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Clinical Reality & Intuition
- Clinical Reality: Understand how real-world clinical data is generated and the gaps between ideal protocols and messy data.
- The Investigative Mindset: Skillfully question partners to uncover "ground truth" in data structures. Audit data to find missing variables, anomalies, or hidden biases.
- Ontology/Immunology Knowledge: Familiarity with cancer progression metrics (e.g., RECIST criteria, TNM staging) and longitudinal treatment lines (e.g., immunotherapy vs. chemotherapy).
What You’ll Bring
The ideal candidate embodies a team-first mentality, exhibiting:
- Independence, curiosity, and attention to detail
- Thriving in fast-paced, dynamic environments
- Ability to lead technical alignment meetings with partners while embracing hands-on code development
Technical & Professional Qualifications
- Education: Bachelor’s or Master’s degree in Life Sciences, Bioinformatics, Health Informatics, Computer Science, Statistics, or a related field. Equivalent industry experience valued.
- Industry Expertise: 3–5+ years of hands-on experience in clinical data management or clinical data engineering within a CRO, CMO, pharma, or bioinformatics environment.Proven ability to convert messy partner data into reproducible, production-ready workflows.
- Coding Proficiency: Expert-level Python, along with expertise in Pandas, NumPy.
- Software Best Practices: Experience with Git version control, reproducibility, and building reusable data pipelines.
- Clinical Data Expertise: Familiarity with EHRs, CRFs, trial data, and domain knowledge of oncology/immunology data.
Partnership & Execution Skills
- Alignment: Strong ability to align on data delivery formats with clinical team partners.
- Start-up Agility: Adaptability to fast-evolving data schemas, ingroups defined from the ground up.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
How to Stand Out
- Cloud Computing Experience: Familiarity with AWS, GCP, or similar platforms.
- Multimodal Datasets: Experience working with clinical records paired with imaging or omic datasets.
- CDISC + Modern Tech-Stack: Knowledge of CDISC standards (SDTM/ADaM), combined with a departure from legacy SAS programming.
- ETL Pipeline Optimization: Experience designing or improving ETL pipelines for large-scale biobanks or multinational consortia.
The Candidate Journey
To apply, submit your CV in English. Our process is transparent and collaborative:
Screening Process
- 30-minute intro call with the Hiring Manager, covering your background, motivations, and role specifics.
Interview Stages
-
Data Strategy Panel Presentation (45 min):
- Present a past data management challenge, such as designing a complex data dictionary or harmonising messy CRO data, followed by Q&A.
-
Technical Deep-Dive (30 min):
- Breakout session with 1–2 Bioptimus engineers exploring your technical expertise.
-
Executive Interview (30 min):
- Discussion with senior leadership focusing on:
- Long-term vision
- Cultural fit
- Mutual potential
- Discussion with senior leadership focusing on:
Offer & Onboarding
- Any** job offer** depends on:
- Successful completion of a reference check.
- Following successful interviews, onsolding begins to integrate you into the Bioptimus ecosystem.
Why This is Unique
What You’ll Get
- Science-Driven Impact: Join a mission-focused team reshaping biology through AI.
- Collaborative Culture: Operate in an autonomous, high-impact environment with:
- Start-up autonomy and fast progress.
- Pioneering research and infrastructure roles at ground level.
- Compensation & Flexibility:
- Competitive salary
- Equity and remote working options.
- Cultural Shaping: Help define scientific and technical norms in a category-defining company.
Our Commitment
We value diversity, equity, and inclusion, ensuring everyone’s input matters. Bioptimus never discriminates based on:
- Race
- Religion
- National origin
- Gender identity/expression
- Sexual orientation
- Age
- Marital status
- Disability status
Hiring decisions are fair, providing equal opportunities to all qualified candidates. We strive to create a welcoming, inclusive environment.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location