Senior Clinical Data Manager

Bioptimus

Clinical Data Manager at Bioptimus, focusing on real-world data and AI model integration. Aiming to enhance biomedical innovation within a fast-growing start-up environment.

Posted 7/2/2026full-timeRemote • 🇩🇪 GermanySeniorWebsite

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 data structures. Proficient in collaborating with external partners and aligning data delivery formats in a dynamic environment.

Highest-signal resume keywords

Python ProgrammingClinical Data ManagementData Quality ControlData HarmonizationGit Version Control

ATS Keywords

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Hard Skills

Data ManipulationData CleaningData ValidationData Dictionary DesignMetadata ModelingBiomedical OntologiesStatistical AnalysisData Pipeline DevelopmentClinical Data StructuresAI-Ready Data Assets

Soft Skills

CollaborationProblem-SolvingAdaptabilityAttention to DetailCommunication

Tools & Technologies

PandasNumPyElectronic Health Records (EHR)Case Report Forms (CRFs)

Industry Keywords

Life SciencesBioinformaticsHealth InformaticsClinical TrialsCROCMOPharmaBiotechOncologyImmunology

Tech Stack

Tools & technologies

NumpyPandasPython

About the role

Key responsibilities & impact

Operate at the intersection of data engineering, clinical science, and partner collaboration across two strategic domains:
Technical conversations with external partners (hospitals, research institutions, CROs/CMOs) and dive into the details of diverse clinical data structures.
Translate ambiguous source data into harmonized, AI-ready assets.
Map and align diverse clinical data to industry-standard biomedical ontologies.
Design, build, and maintain data dictionaries, schemas, and metadata models.
Establish, automate, and enforce data quality control (QC) and validation frameworks for incoming partner data.
Write production-grade Python code to automate data cleaning and harmonization tasks.
Practical understanding of how clinical data is generated in the real world.
Identify anomalies and hidden biases in incoming data.

Requirements

What you’ll need

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.
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.
High proficiency in Python and standard data science libraries (e.g., Pandas, NumPy) for data manipulation, cleaning, and validation.
Demonstrated commitment to code reproducibility, including strong experience with Git version control and building reusable data pipelines.
Familiarity with clinical data structures, electronic health records (EHR), case report forms (CRFs), and longitudinal clinical trial data.
Knowledge of standard clinical and biological ontologies, specifically those tailored to cancer/oncology and/or immunology datasets.
Ability to align on data delivery formats with a partner clinical teams.
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, and flexibility (remote options)