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Remote Clinical Data Code, Oncology

Remote-first Full-time Now hiring

About the job Remote Clinical Data Code, Oncology Remote Clinical Data Code, Oncology needs 2+ years of experience in clinical data coding, preferably in oncology trials. Clinical Data Code, Oncology requires:

  • Part time
  • 20 hours weekly
  • Bachelors degree in Life Sciences, Health Information Management, or related field.
  • Strong understanding of clinical trial data standards and regulatory requirements.
  • Excellent attention to detail and ability to manage multiple coding tasks in a fast-paced environment.
  • Results-driven, take initiative and ownership to accomplish work. Knowledge of ICH, Good Clinical Practice and FDA regulations.
  • Proficiency with Rave Coder and familiarity with MedDRA and WHO Drug dictionaries. Effective time management and organization skills.
  • Strong communication skills for cross-functional collaboration with CRAs, CDMs, and medical reviewers.
  • Experience with coding in global, multi-site oncology studies. Good interpersonal, written and verbal communication skills.

Clinical Data Code, Oncology duties:

  • Perform ongoing medical and medication coding using Rave Coder in accordance with MedDRA and WHO Drug dictionaries.
  • Review and resolve auto-coded and manually coded terms, ensuring alignment with SMPA coding conventions and internal SOPs
  • Collaborate with Clinical Data Managers to clarify ambiguous or unclear verbatim terms and issue coding queries when necessary
  • Maintain coding listings and ensure all terms are coded and approved prior to database lock
  • Support coding-related documentation and contribute to the development and maintenance of coding guidelines and SOPs
  • Participate in system validation, user acceptance testing, and updates related to coding modules and dictionary integrations.
  • Review and resolve complex or ambiguous verbatim terms, escalating to medical reviewers or clinical teams as needed.
  • Monitor coding metrics and quality indicators, proactively identifying trends and areas for improvement.
  • Ensure adherence to Data Management standards

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