Senior Clinical Data Manager

Bioptimus

Clinical Data Manager bridging real-world clinical data to AI models at Bioptimus. Structure datasets, enforce data quality, and collaborate globally in a fast-paced environment.

Posted 7/2/2026full-timeRemote • 🇬🇧 United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in clinical data management and engineering, with a strong focus on Python programming, data quality control, and the application of biomedical ontologies. Proficient in structuring and harmonizing clinical datasets for AI readiness while ensuring reproducibility and compliance with industry standards.

Highest-signal resume keywords

Python ProgrammingClinical Data ManagementData Quality ControlBiomedical OntologiesGit Version Control

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data CleaningData HarmonizationData Dictionary DesignData Schema DevelopmentStatistical AnalysisReproducible Code WritingData Pipeline BuildingData AuditingClinical Data StructuresLongitudinal Clinical Trial Data

Soft Skills

CollaborationCommunicationAdaptabilityProblem-Solving

Tools & Technologies

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

Industry Keywords

Life SciencesBioinformaticsHealth InformaticsPharmaBiotechCROCMOCancerOncologyImmunology

Tech Stack

Tools & technologies

NumpyPandasPython

About the role

Key responsibilities & impact

Bridge the gap between unstructured, real-world data, and AI models
Structure clinical datasets
Write reproducible code
Enforce incoming data quality control
Design data dictionaries and ontologies
Participate in technical conversations with external partners
Translate ambiguous source data into harmonized, AI-ready assets
Map 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 frameworks
Write production-grade Python code for data cleaning and harmonization
Audit data to find missing variables, anomalies, and hidden biases
Utilize clinical data progression metrics

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
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)
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
Flexibility (remote options)