Scientist Real World Evidence  

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The Scientist Real-World Evidence supports observational and population‑based research by delivering high‑quality statistical analyses and analytical documentation. The role contributes to the development of analysis plans, preparation of analysis‑ready datasets, and execution of quantitative analyses to support scientific and business objectives. The position operates with a high degree of independence while collaborating closely with internal scientific, analytics, and project stakeholders. This is a temporary role intended to ensure continuity of analytical delivery during a defined coverage period.

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Key Responsibilities

Statistical Analysis & Methodology
- Contribute to the development, review, and maintenance of statistical and analytical analysis plans for observational and real‑world studies.
- Perform quantitative analyses using appropriate statistical methods, including analyses of population‑based and survey data where applicable.
- Implement analyses using R, ensuring efficient, reproducible, and well‑documented code.
- Assess and document analytical assumptions, methodological choices, and their implications for interpretation of results.

Data Preparation & Quality
- Prepare, manage, and validate analysis‑ready datasets using R‑based workflows.
- Develop and maintain reproducible data‑processing pipelines, including variable derivations and transformations.
- Perform data quality checks and ensure traceability between source data, derived variables, and analytical outputs.
- Maintain clear and well‑documented analytical assets to support reproducibility and handover.

Collaboration & Communication
- Work collaboratively with internal stakeholders, including statisticians, data scientists, epidemiologists, and project leads.
- Participate in regular project and working meetings, providing clear updates on analytical progress and risks.
- Communicate complex statistical concepts, methods, and results clearly to technical audiences.

Documentation & Delivery
- Produce clear analytical documentation, including commented R scripts and supporting materials, to enable internal review and reuse.
- Ensure timely delivery of assigned analytical tasks in line with agreed priorities and timelines.

MUST HAVE (Required Qualifications & Skills)
- Advanced degree (Master's or PhD preferred) in biostatistics, statistics, epidemiology, data science, or a related quantitative field.
- Advanced programming skills in R, including writing efficient, readable, and reproducible analytical code.
- Demonstrated experience conducting statistical analyses for observational or real‑world data.
- Experience contributing to or authoring statistical or analytical analysis plans.
- Strong data management skills, including creation and maintenance of analysis‑ready datasets.
- Ability to work independently, manage multiple analytical tasks, and deliver results with limited supervision.
- Strong written and verbal communication skills for technical and scientific audiences.

NICE TO HAVE (Preferred Qualifications & Skills)
- Experience with large‑scale population or survey datasets.
- Familiarity with real‑world evidence or public‑health research environments.
- Experience developing reusable R functions, analytical frameworks, or packages.
- Experience working in matrixed or cross‑functional research teams.
- Prior experience in regulated or highly documented analytical environments.
- Ability to quickly onboard to existing analytical workflows, coding standards, and documentation practices.

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We look forward to discovering your profile and exploring the possibility of welcoming you to our client Philip Morris.