Senior Statistical Programmer (R) — Lead CDISC SDTM/ADaM

ICON Strategic Solutions; Warszawa; On-site; PLN 180,000 - 280,000; Full time

Job summary

ICON plc is a world-leading healthcare intelligence and clinical research organization.

We are seeking a Senior Statistical Programmer to join our diverse team in Poland, focusing on developing and validating R programs for clinical trial data and generating outputs in CDISC SDTM/ADaM format.

You will lead one or more programming teams, collaborate with biostatisticians, and ensure quality, regulatory compliance, and efficient, reusable R workflows across studies.

Qualifications

• Bachelor's or Master's degree in a quantitative field.
• Experience in statistical programming within a clinical research, biotechnology, or pharmaceutical environment.
• Strong proficiency in R programming and CDISC standards.

Responsibilities

• Developing and validating R programs for statistical analysis and reporting of clinical trial data.
• Leading more than one Statistical Programming team to create, validate and generate statistical outputs (tables, listings, figures).
• Collaborating with biostatisticians to define analysis plans and methods.
• Performing quality control and validation of statistical outputs for regulatory compliance.
• Assisting in preparation of statistical reports and submissions.
• Contributing to process improvements and automation using R-based workflows.

Skills

R programming

Statistical analysis

Team leadership

Communication

Education

Bachelor's or Master's degree in Statistics/Mathematics/Computer Science/Life Sciences

Tools

CDISC SDTM/ADaM

Job description

ICON plc is a world-leading healthcare intelligence and clinical research organization.

We are seeking a Senior Statistical Programmer to join our diverse team in Poland, focusing on developing and validating R programs for clinical trial data and generating outputs in CDISC SDTM/ADaM format.

You will lead one or more programming teams, collaborate with biostatisticians, and ensure quality, regulatory compliance, and efficient, reusable R workflows across studies.