The Statistical Programmer II provides technical expertise for the conduct of clinical trials, and works with minimal supervision to support various programming activities related to the analysis and reporting of clinical study data. In addition, the Statistical Programmer II may fill the Statistical Programming Lead role (or part of that role) on small, non-complex projects.

This role supports the generation of real-world evidence (RWE) by programming and analyzing large-scale observational datasets. The ideal candidate will have strong SAS programming skills, familiarity with R, and experience working with healthcare claims, electronic health records (EHR), or registry data.

Key Accountabilities:
Project Management:

• Assist in the coordination of project start-up activities, creation of global programs, tracking spreadsheets, and other required documentation.

Statistical Programming for Assigned Projects:

• Deliver best value and high quality service.

• Check own work in an ongoing way to ensure first-time quality.

• Use efficient programming techniques to produce derived datasets (e.g. SDTM, ADaM), tables, figures, and data listings of any complexity and QC low-medium complexity derived datasets, tables, figures, and data listings.

• Assist in the production/QC of derived dataset specifications and other process supporting documents and submission documentation.

Training:

• Maintain and expand local and international regulatory knowledge within the clinical industry.

• Develop knowledge of SAS and processes/procedures within other Parexel functional areas.

• Provide relevant training and mentorship to staff and project teams as appropriate.

General:

• Develop, validate, and maintain SAS and R programs to support RWD analyses, including prevalence, treatment patterns, cost/utilization, and time-to-event studies 

• Execute programming tasks using Client standard macros and environments within UNIX and AWS-based platforms 

• Perform double programming and quality control (QC) checks in alignment with internal SOPs and KIMS system workflows 

• Collaborate with statisticians, data scientists, and cross-functional teams to define specifications and deliverables 

• Document programming processes and outputs in accordance with regulatory and internal audit requirements

• Contribute to the development and maintenance of internal R packages, Shiny apps, and Quarto documentation to support programming workflows 

• Participate in onboarding and mentoring of new programmers, including training on client-specific tools and data environments 

Skills:

• Excellent analytical skills.

• Proficiency in SAS; working knowledge of R is highly desirable

• 3+ years of experience in statistical programming, preferably in a pharmaceutical or healthcare setting

• Knowledge and understanding of the programming and reporting process.

• Knowledge of SOPs/Guidelines, ICH-GCP, and any other applicable local and international regulations such as 21 CFR Part 11.

• Familiarity with real-world data sources such as Optum, MarketScan, Flatiron, CPRD, or similar 

• Experience with Snowflake, UNIX/Linux environments, and version control tools (e.g., Git).

• Strong understanding of data privacy, regulatory compliance, and audit-readiness in RWD contexts 

• Ability to learn new systems and function in an evolving technical environment.

• Ability to manage competing priorities and flexibility to change.

• Attention to detail.

• Ability to successfully work as part of a global team.

• Work effectively in a quality-focused environment.

• Effective time management in order to meet daily metrics or team objectives.

• Show commitment to and perform consistently high quality work.

• Business/operational skills that include customer focus, commitment to quality management, and problem solving.

Knowledge and Experience:

• Competent in written and oral English.

• Good communication skills.

• Experience with OMOP/OHDSI standards and tools

• Exposure to project management tools like Monday.com

• Ability to work independently and manage multiple priorities in a fast-paced environment

Education:

• Educated to degree level in a relevant discipline and/or equivalent work experience; Bachelor’s or Master’s degree in Statistics, Computer Science, Epidemiology preferred.