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- Tasks: Support clinical trials by developing and validating clinical data systems.
- Company: Join a team dedicated to advancing medical research and patient care.
- Benefits: Gain hands-on experience in a dynamic environment with opportunities for growth.
- Why this job: Make a real impact on healthcare while collaborating with experts in the field.
- Qualifications: Bachelor's or Master’s in Computer Science, Bioinformatics, Statistics, or related field required.
- Other info: Proficiency in SAS, R, Python, or SQL is essential; experience with clinical databases preferred.
The predicted salary is between 36000 - 60000 £ per year.
About the Role:
The Clinical Programmer plays a key role in supporting clinical trials by developing and validating clinical data systems. Key duties include programming study databases, extracting data, and ensuring data integrity and regulatory compliance. This position will collaborate with data managers, statisticians, and researchers to support accurate data collection, analysis, and reporting, aiding in medical advancements and patient care. Strong programming and data management skills are essential for optimizing clinical processes.
Responsibilities
- :Develops programs to extract, integrate, and analyse data, generating specified outputs
- .Provides programming input throughout clinical study phases, from design to publication
- .Assists in setting up and maintaining software like Veeva for database programming and edit checks
- .Ensures data integrity by overseeing available data sources
- .Manages programming activities across studies, including external oversight when outsourced
- .Supports clinical data management tasks, such as eCRF and EDC system development
- .Contributes to quality standards and follows GCP practices
- .Reports to the Head of Clinical Operations and collaborates with the senior data manager
.
Qualification
- s:Education: Bachelor's or Master’s in Computer Science, Bioinformatics, Statistics, or related fiel
- d.Technical Skills: Proficiency in SAS, R, Python, or SQL; experience with clinical databases (e.g., Oracle Clinical, Medidata Rave); familiarity with CDISC standards (SDTM, ADaM
- ).Experience: Clinical programming experience in pharmaceuticals or CROs, with knowledge of CDISC, SDTM, and ADaM standard
- s.Regulatory Knowledge: Understanding of GCP, FDA, EMA, and related clinical data guideline
- s.Soft Skills: Strong communication and analytical skills for engaging with project teams, gathering requirements, and translating them into data plans; ability to work collaboratively with teams in Biostatistics, Clinical Data Management, Pharmacovigilance, and other area
s.
If this opportunity sounds of interest please reach out to me bel
ow:
.uk
Clinical Programmer employer: AL Solutions
Contact Detail:
AL Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Clinical Programmer
✨Tip Number 1
Familiarize yourself with the specific programming languages and tools mentioned in the job description, such as SAS, R, Python, and SQL. Having hands-on experience or projects that showcase your skills in these areas can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the clinical programming field, especially those who have experience with CDISC standards and clinical databases. Engaging with them on platforms like LinkedIn can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Stay updated on the latest trends and regulations in clinical trials, particularly GCP, FDA, and EMA guidelines. This knowledge will not only help you in interviews but also demonstrate your commitment to the field.
✨Tip Number 4
Prepare to discuss specific examples of how you've contributed to data integrity and quality standards in previous roles. Being able to articulate your experience in managing programming activities and collaborating with cross-functional teams will be crucial during the interview process.
We think you need these skills to ace Clinical Programmer
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description for the Clinical Programmer position. Understand the key responsibilities and required skills, such as programming in SAS, R, Python, or SQL, and familiarity with clinical databases.
Tailor Your CV: Customize your CV to highlight relevant experience in clinical programming and data management. Emphasize your proficiency with the required programming languages and any experience you have with CDISC standards.
Craft a Strong Cover Letter: Write a compelling cover letter that showcases your passion for clinical trials and your ability to contribute to medical advancements. Mention specific projects or experiences that demonstrate your skills in data integrity and regulatory compliance.
Highlight Soft Skills: In your application, don't forget to mention your strong communication and analytical skills. Provide examples of how you've successfully collaborated with teams in previous roles, as this is crucial for the Clinical Programmer position.
How to prepare for a job interview at AL Solutions
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in programming languages like SAS, R, Python, or SQL. Highlight any relevant experience with clinical databases and how you've utilized these skills in past projects.
✨Understand Regulatory Guidelines
Familiarize yourself with GCP, FDA, and EMA guidelines. Be ready to explain how you ensure compliance in your work and provide examples of how you've navigated regulatory requirements in previous roles.
✨Demonstrate Collaboration Skills
Since the role involves working closely with data managers, statisticians, and researchers, prepare to share examples of successful teamwork. Discuss how you gather requirements and translate them into actionable data plans.
✨Prepare for Problem-Solving Questions
Expect questions that assess your analytical skills and ability to troubleshoot issues in clinical data management. Think of specific challenges you've faced and how you resolved them, particularly in relation to data integrity and quality standards.
