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.

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.

Senior Clinical Data Manager

Key skills