Senior Clinical Data Manager

Bioptimus

Clinical Data Manager connecting clinical data with AI models at Bioptimus. Responsible for harmonizing data pipelines and ensuring data quality across initiatives.

Posted 7/13/2026full-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 the application of biomedical ontologies in cancer and immunology. Proven ability to collaborate with global partners to standardize data pipelines and ensure data readiness for AI applications.

Highest-signal resume keywords

Python ProgrammingData Quality Control (QC)Clinical Data ManagementBiomedical OntologiesData Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ManipulationData CleaningData ValidationReproducible CodeData Dictionary DesignData Schema DevelopmentAutomated Data ProcessingStatistical AnalysisVersion Control (Git)Data Auditing

Soft Skills

CollaborationCommunicationProblem-SolvingAdaptability

Tools & Technologies

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

Industry Keywords

Clinical Data EngineeringCROCMOPharmaBiotechCancer Progression MetricsLongitudinal Clinical Trial Data

Tech Stack

Tools & technologies

NumpyPandasPython

About the role

Key responsibilities & impact

Bridge the gap between unstructured, real-world data, and frontier AI models
Serve as the technical link during conversations with global partners to standardise and harmonise data pipelines
Structure clinical datasets within the STELA program
Write reproducible code, enforce incoming data QC, and design data dictionaries and ontologies
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
Design, build, and maintain data dictionaries, schemas, and metadata models
Establish, automate, and enforce data quality control (QC) frameworks
Write production-grade Python code to automate data cleaning and harmonization tasks
Actively audit data to identify missing variables, anomalies, and hidden biases
Familiarity with cancer progression metrics.

Requirements

What you’ll need

Bachelor’s or Master’s degree in Life Sciences, Bioinformatics, Health Informatics, Computer Science, Statistics, or related quantitative field
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 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)