Clinical Data Manager (Senior)

Bioptimus’ mission is to accelerate biomedical innovation by building the reference foundation model of biology that will unlock AI superpowers for the biomedical ecosystem. As a well-funded and fast-growing start-up headquartered in Paris and incorporated in October 2023, we are growing a world-class team of scientists, engineers, and product leaders.

This is a remote role. We’re headquartered in Paris, but the position can be performed remotely outside of Paris.

We are 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 be the authority on clinical data structures, serving as the technical link during conversations with our global partners to standardise and harmonise data pipelines.

Operating within our STELA program, you will structure our clinical datasets. You are a hands-on technical expert who writes reproducible code, enforces incoming data QC, and designs the data dictionaries and ontologies for our models.

We recently launched the Spatial Tissue Embedding Learning Atlas (STELA)—a multinational spatial data generation initiative anchored by strategic partnerships with 10x Genomics and Broad Clinical Labs. STELA serves as the data backbone for M-Optimus, aiming to profile up to 100,000 patient specimens across three continents (US, Europe, and Asia). This will integrate high-resolution spatial transcriptomics, histopathology imaging, and longitudinal clinical records to bring forward the next era of biological AI and precision medicine.

As our Clinical Data Manager, you will operate at the intersection of data engineering, clinical science, and partner collaboration across two strategic domains:

• Technical Partner Interface: Participate directly in technical conversations with external partners (hospitals, research institutions, CROs/CMOs). Dive into the details of diverse clinical data structures to understand how data is captured, stored, and extracted.
• Order from Uncertainty: Translate ambiguous source data into harmonized, AI-ready assets.
• Ontology Integration: Map and align diverse clinical data to industry-standard biomedical ontologies (e.g., SNOMED, ICD, etc…) with an emphasis on clinical oncology and immunology data.

• Data Dictionary Architecture: Design, build, and maintain data dictionaries, schemas, and metadata models that align with STELA’s multimodal pipeline requirements, while ensuring integration with existing pipelines.
• Enforcing Ingest Quality: Establish, automate, and enforce data quality control (QC) and validation frameworks to check incoming partner data for integrity, completeness, and programmatic consistency.
• Reproducible Pipeline Code: Write production-grade Python code to automate data cleaning and harmonization tasks.

• Clinical Reality: Practical understanding of how clinical data is generated in the real world (hospitals, trials, CROs). You understand the gaps between ideal protocols and messy clinical realities, and you know what red flags to look for in incoming data.
• The Investigative Mindset: You know what questions to ask partners to get to the "ground truth" of their data structures. Actively audit data to find missing variables, anomalies, and hidden biases.
• Oncology/Immunology Domain Knowledge: Familiarity with cancer progression metrics (e.g., RECIST criteria, TNM staging, longitudinal treatment lines like immunotherapy vs. chemotherapy) so you can recognize what data is important.

The successful candidate will have a ‘team-first’ attitude; be independent, curious, and detail-oriented; thrive in a dynamic, fast-paced environment; and be fun to work with. You possess the rare ability to confidently lead complex technical alignment meetings with partners while simultaneously being excited to roll up your sleeves and write code.

• Educational Background: Bachelor’s or Master’s degree in Life Sciences, Bioinformatics, Health Informatics, Computer Science, Statistics, or a related quantitative field. Equivalent practical industry experience is highly valued.
• Industry Experience: A few years (typically 3–5+) of hands-on experience in clinical data management or clinical data engineering within a CRO, CMO, pharma, or biotech environment. Proven track record of taking messy partner data and building reproducible, production-grade workflows.
• Hands-on Coding Skills: High proficiency in Python and standard data science libraries (e.g., Pandas, NumPy) for data manipulation, cleaning, and validation.
• Software Best Practices: Demonstrated commitment to code reproducibility, including strong experience with Git version control and building reusable data pipelines.
• Clinical Data Expertise: Familiarity with clinical data structures, electronic health records (EHR), case report forms (CRFs), and longitudinal clinical trial data.
• Ontologies & Vocabularies: Knowledge of standard clinical and biological ontologies, specifically those tailored to cancer/oncology and/or immunology datasets.

• Communication & Alignment: Ability to align on data delivery formats with a partner clinical teams.
• Start-up experience: Comfort working in a fast-paced startup environment where data schemas evolve and ingest requirements must be defined from scratch.

• Experience with cloud computing platforms (AWS, GCP, etc…)
• Experience working directly with multimodal datasets (e.g., matching clinical records with omics or digital pathology imaging).
• Understanding of CDISC standards (SDTM/ADaM) combined with a modern tech-stack approach (beyond legacy SAS programming).
• Experience building or optimizing ETL pipelines for large-scale biobanks or multinational clinical consortia.

To be considered, please submit your CV in English. We believe in a transparent and collaborative interview process. Here is what you can expect after submitting your application:

• Screening: A 30-minute introductory call with the Hiring Manager to discuss your background, motivations, and the position in more detail.
• Interviews: Following a successful screening, you will be invited to a series of interviews:
  1. Data Strategy Panel Presentation (45 min): You will present a short overview of a past data management challenge you overcame (e.g., designing a complex data dictionary or aligning messy CRO data), followed by Q&A.
  2. Technical Deep Dive (30 min) - There will be 1 additional break out session to do a deep dive with 1-2 Bioptimus Engineers
  3. Executive Interview (30 min): A discussion with member(s) of our Senior Leadership focusing on long-term vision, cultural fit, and mutual potential.
• Offer: Following the completion of all interviews, our hiring team will make a final decision. Please note that an offer is contingent upon the successful completion of a reference check.
• Onboarding: Welcome to the team! We will begin your onboarding to get you fully integrated at Bioptimus!

• Join a mission-driven team redefining biology through AI.
• Work in a collaborative, high-autonomy, high-impact environment.
• Contribute to pioneering research, infrastructure, or strategy at the ground floor.
• Competitive compensation, equity, and flexibility (remote options).
• Help shape the scientific and technical culture of a category-defining company.