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Job Function:
Discovery & Pre-Clinical/Clinical Development
Job Sub Function:
Pharmacokinetics & Pharmacometrics
Job Category:
Scientific/Technology
All Job Posting Locations:
High Wycombe, Buckinghamshire, United Kingdom
Job Description:
About Innovative Medicine
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.
Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Learn more at https://www.jnj.com/innovative-medicine
Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s):
Belgium - Requisition Number: R-060352
United States - Requisition Number: R-057852
Remember, whether you apply to one or all of these requisition numbers, your applications will be considered as a single submission.
We are searching for the best talent for a Senior Scientist, Pharmacometrics to be located in High Wycombe, United Kingdom.
Purpose: The position is responsible to support the Pharmacometrics Leader (PML) or Clinical Pharmacology Leader (CPL) who is the modeling lead in development and execution of Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling and Simulation activities related to the research, design, implementation, data analysis, interpretation, reporting, and publication of Clinical Pharmacology and Pharmacometrics (CPP) sponsored and -supported studies for products in any phase of development. The Pharmacometric (PM) support team is mainly passionate about data programming, data quality control (QC), analysis QC and e-submission related aspects.
You will be responsible for:
- Preparing R programming scripts to generate non-linear mixed effect modelling (NONMEM) analysis input dataset(s) for PK, PK/PD or Exposure Response analysis, based on requests from PM leader or Modeling leader. During dataset generation, PM support also modifies the variable definition file (PM leader or CPP leader is the main author of this document) which clearly defines each variable within this dataset with any additional information as they see fit. The NONMEM input dataset(s) created could be for interim or final analysis. The source used could be interim (uncleaned) or final Study Data Tabulation Model/Analysis Data Model (SDTM/ADAM) datasets or in sources in other formats, in some cases extensive data cleaning and complex calculations are needed.
- Generating Analysis Dataset Non-Compartmental Analysis (ADNCA) input datasets and associated metadata.
- Upon request, QC NONMEM or ADNCA input dataset(s) generated by another PM support colleague. Log which QC script is used, which subjects were checked per study, what other aspects were checked within the dataset, the findings of the QC and the follow-up actions of those findings in a QC document.
- Generating e-submission package for NONMEM (or other modelling type) analysis. In general, the package includes NONMEM input datasets, NONMEM control file, output parameter files, output table files and other files, in addition to supporting documents such as define and var-names-descr files. PM support renames the files provided by PM leader or CPP leader, so they fit the naming convention requirements for e-submission package if needed, converts these files into the appropriate formats, and places them into the right folder structure then links them to the define and var-names-descr files. PM support works closely with EPOD team to ensure the e-submission package has the right structure, accurate formats and being placed in the right assembly server directory.
- Interacting with other departments (including but not limited to Data Management, Clinical & Statistical Programming, bio-analytical teams, Regulatory) and external Vendors to communicate the needs of CPP in data collection, data formatting, data representation and cross departmental trainings if needed. Promote better understanding across different departments.
- Piloting the generation of the first draft AI Authored data specification.
- Performing pharmacometric Analysis QC activities in support of the PM Leader.
- Improving CPP internal processes in dataset creation, dataset QC (e.g. a standard QC R script with a checklist), pharmacometric analysis QC procedures and e-submission package preparation; move forward existing automatizations ensuring high quality & speed.
- Presenting (cross-departmental) process improvements within/outside the department.
- Carrying out functional responsibilities in accordance with applicable SOPs and Regulatory requirements.
Qualifications / Requirements:
Education:
- A minimum of a Master's degree in Pharmaceutical Sciences, Clinical Pharmacology, Chemical/Biomedical Engineering or a related field with 5+ years of pharmaceutical development experience OR a PhD 2+ years of pharmaceutical development experience is required.
Experience and Skills:
Required:
- Expertise with R programming language and R function creation is required.
- A fundamental understanding of clinical drug development and PK & PK/PD population PK modeling concepts is required.
- Good understanding of regulatory requirements and eCTD is required.
- Data-handling skills and solid understanding of clinical database structures such as SDTM and ADAM is required.
- Excellent written and oral communication skills with strong attention to detail are required.
- Ability to build and improve working relationships inside and outside the department; ability and willingness to work in a cross-functional team environment (building strategic working relationships) are required.
- Ability to collaborate in a diverse global environment is required.
- Self-motivation with the ability to work independently with minimal direction is required
Preferred:
- R Shiny programming experience is strongly preferred.
- Knowledge of principles of non-linear mixed effect modelling is preferred.
- Prior experience with pharmacometric analysis QC would be an asset.
- Experience within oncology and immunology is an asset.
- Prior use of AI prompting to generate R code & functions would be an asset.
Required Skills:
Preferred Skills:
Analytical Reasoning, Clinical Data Management, Clinical Pharmacology, Clinical Trials Operations, Coaching, Critical Thinking, Drug Discovery Development, Empowering People, Pharmacokinetic Modeling, Pharmacokinetics, Pharmacology, Pharmacometrics, Program Management, Report Writing, Scientific Research, Technologically Savvy
