Senior Clinical Data Manager

Bioptimus

Clinical Data Manager coordinating data engineering and clinical science to standardize data for AI models. Join Bioptimus in reshaping biomedicine with AI-driven insights.

Posted 7/13/2026full-timeRemote • 🇫🇷 FranceSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in clinical data management and engineering, with a strong focus on Python programming, data quality control, and harmonization of clinical oncology and immunology datasets. Proficient in collaborating with external partners and aligning data delivery formats in a fast-paced startup environment.

Highest-signal resume keywords

Python ProgrammingClinical Data ManagementData Quality ControlBiomedical OntologiesData Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data CleaningData HarmonizationData Dictionary DesignMetadata ModelingStatistical AnalysisData ValidationData AuditingClinical Data StructuresElectronic Health RecordsStandard Data Science Libraries

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

Git Version ControlPandasNumPy

Industry Keywords

Clinical TrialsOncologyImmunologyCROCMOPharmaBiotechHealth InformaticsBioinformaticsLife Sciences

Tech Stack

Tools & technologies

NumpyPandasPython

About the role

Key responsibilities & impact

Operate at the intersection of data engineering, clinical science, and partner collaboration
Participate directly in technical conversations with external partners (hospitals, research institutions, CROs/CMOs)
Translate ambiguous source data into harmonized, AI-ready assets
Map and align diverse clinical data to industry-standard biomedical ontologies with an emphasis on clinical oncology and immunology data
Design, build, and maintain data dictionaries, schemas, and metadata models
Establish, automate, and enforce data quality control (QC) and validation frameworks
Write production-grade Python code to automate data cleaning and harmonization tasks
Understand how clinical data is generated in real-world settings
Actively audit data to find missing variables, anomalies, and hidden biases
Recognize important data in clinical trials related to oncology/immunology

Requirements

What you’ll need

Educational Background: Bachelor’s or Master’s degree in Life Sciences, Bioinformatics, Health Informatics, Computer Science, Statistics, or a related quantitative field
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
Hands-on Coding Skills: High proficiency in Python and standard data science libraries (e.g., Pandas, NumPy)
Software Best Practices: Demonstrated commitment to code reproducibility, including 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 tailored to cancer/oncology and/or immunology datasets
Communication & Alignment: Ability to align on data delivery formats with 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.

Benefits

Comp & perks

Competitive compensation
Equity
Flexibility (remote options)