Data Scientist / Biostatistician – Clinical & Real World Data (Pharma / Biotech)

We are looking for a Data Scientist or Biostatistician with experience working on clinical trial and/or real-world healthcare data. In this role, you will support data analysis and insight generation across clinical and real-world datasets, helping translate complex healthcare data into meaningful outputs for decision-making. You will work closely with cross-functional teams in a data-driven environment, contributing to projects related to clinical studies, real-world evidence (RWE), and healthcare data analysis.

• 3–5+ years of hands-on experience working with clinical trial and/or real-world data
• Experience in pharma, biotech, CRO, or healthcare-related environments
• Practical experience working with datasets such as EHR, claims, registries, or clinical trial data
• Experience with Python, R, SQL, or SAS
• Strong data analysis and data manipulation skills
• Understanding of statistical methods or data science approaches applied to healthcare data

Nice to have:

• Familiarity with clinical data standards such as CDISC (SDTM, ADaM), OMOP, or HL7
• Experience in real-world evidence (RWE) or real-world data (RWD) projects
• Experience working with large-scale healthcare datasets
• Strong analytical mindset and attention to data quality
• Ability to work with complex, real-world datasets
• Comfortable collaborating with both technical and non-technical stakeholders
• Detail-oriented and able to work in regulated environments
• Good communication skills in English

• Analyse clinical trial and/or real-world datasets (e.g. EHR, claims, registries)
• Prepare, transform, and validate healthcare datasets for analysis and reporting
• Apply statistical or data science methods to generate insights from clinical data
• Work with structured and semi-structured healthcare data sources
• Support development of data pipelines and analytical workflows
• Collaborate with stakeholders to understand analytical needs and translate them into data solutions
• Contribute to data standardisation and transformation processes (e.g. CDISC, OMOP – if applicable)
• Ensure data quality, consistency, and compliance with industry standards