Bioptimus
Clinical Data Manager (Senior)
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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 more than $75M in funding, Bioptimus is a fast-growing startup 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.
About the Role
We are seeking a technical, execution-focused Clinical Data Manager to bridge the gap between unstructured, real-world data and our frontier AI models. This role focuses on clinical data architecture, serving as the technical bridge to standardise and harmonise data pipelines with global partners.
This is a remote role, supporting Bioptimus’ HQ in Paris.
Our interdisciplinary STELA program (Spatial Tissue Embedding Learning Atlas) drives this effort—partnering with 10x Genomics and Broad Clinical Labs to generate multimodal data from 100,000 patient specimens across three continents (USA, Europe, Asia). Specimens will integrate high-resolution spatial transcriptomics, histopathology imaging, and longitudinal clinical records to advance biological AI and precision medicine.
What You’ll Do
As Clinical Data Manager, you’ll operate at the intersection of clinical science, technical collaboration, and data engineering:
1. Partner Data Engineering & Collaboration
- Drive interface: Facilitate technical conversations with external partners (hospitals, research institutions, CROs/CMOs), deeply understanding clinical data structures and processes.
- Harmonise messy data: Translate disparate, ambiguous source data into AI-ready, structured formats.
- Ontology alignment: Map diverse clinical data to standard ontologies (SNOMED, ICD, etc.), focusing on oncology and immunology expertise.
2. Data Governance, Quality & Automation
- Harmonise clinical datasets: Design and maintain data dictionaries, metadata models, and schemas tailored to STELA’s multimodal pipelines and broader integration needs.
- Enforce rigorous QC: Automate data validation frameworks to uphold quality, completeness, and consistency of incoming clinical data.
- Build reusable pipelines: Write production-grade Python code for automated data cleaning and harmonisation.
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3. Clinical Insight & Expertise
- Real-world data awareness: Recognise idiosyncrasies of clinical ecosystems (hospitals, trials, CROs, etc.) and flag anomalies with acute detail awareness.
- Clinical-first mindset: Bonus experience with/knowledge of oncology metrics (RECIST/TNM staging), immunotherapy vs. chemotherapy treatment lines, and investigator clinical reasoning.
What You’ll Bring
You embody our team-collaborative ethos—guided by curiosity, a keen eye for detail, and a fun problem-solving attitude. More specifically:
Technical & Professional Qualifications
- Education: Bachelor’s or Master’s degree in Life Sciences, Bioinformatics, Health Informatics, Computer Science, Statistics, or related; prior work experience highly recommended.
- Clinical CRM/Engineering: 3–5+ years managing raw clinical data structures in biopharma, CRO, CMO, or research environments. Proven ability to manage error-prone data into reproducible, production-ready workflows.
- Python: Spaße in Pandas/NumPy for cleaning, validation, and standardising clinical tabular data.
- Versioning & Pipeline Design: Equally comfortable crafting human-readable data dictionaries as plotting reusable, scalable ETL loops via Git.
- Ontologies & Standards: Proven knowledge of CDISC (SDTM/ADaM), FAIR principles, open-faith semantic ontologies (+ healthcare-specific ontologies like SNOMED/LOINC).
Soft Skills
- Proven ability to lead technical alignment meetings with diverse stakeholders.
knowledgecase-commitment: Calm in ambiguity, wildstart-driven progress in messy environments.
How You’ll Stand Out
- Experience with cloud-based compute (AWS/GCP) and auto-scaling analytics pipelines.
- Familiarity with spatiotemporal clinical record linkage (e.g. linking molecular omics to patient longitudinal reports).
- CDISC + modern-tech enthusiasm (e.g. PySpark > legacy SAS as well as cloud-native versioned metadata)—aiming for semi-structured/NoSQL schema (e.g., Parquet for ETL, temporal snapshots).
- Antipathy for manual labor in biobanks/consortia: Proven success scaling federated-decentralised clinical ingest.
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Candidate Journey
Step 1: Share CV. Awards or lists = unnecessary after diagrams. Step 2:
- Screening call (30m) -- quit conversations with our hiring manager.
- Data Challenge Interview (45m) -> You walk 2 pieces: Present how you’ve built/resolved a “data-conundrum” (e.g., aligning messy CRO forms by hand) then Q&A via Whiteboard-mode.
- Technical Deep Dive (30m) – Scheduled with 1–2 Bioptimus engineers to debug/review a mock clinical ingestion pipeline.
- Executive Chat (30m) – Realign 2 judgement calls with leadership over high-level mission & culture fit.
✅ offer: Locked in after a post-check.
Why Bioptimus
- Join the coterie leading the first biological Un*x kernel for AI. Build the plumbing that democratises 🔬MR-tweezery.
- Work in a flat, STARTUP speed but ready for-scale "company-owned team-owned" ethos--think "zoom-beyond-zoomies".
- By row up your sleeves with scientists or with engineering--both equally respected channels.
- Peer-leading compensation (at a “Level 5” framing) + long-duration equity.
- Flexibility: Role is 100% remotely-based. Competencies for global time zones are valued.
** Creed:** Lifesaves are the result: we are deliberate how we make every hire and culture value.
Bioptimus is committed to unceasing growth and equal opportunity. Apply with confidence—our hiring is fully separated from bias checks. abgelehnen deviation will be re-trained.
Inclusive culture: Decisions on employees’ experiences are shaped equal across disney-diversities and standpoint-built (not tokenism-y).
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