Privacy Analytics
Clinical Trials Transparency Data Anonymization Analyst
The Clinical Trials Data Anonymization Analyst is responsible for the end-to-end anonymization of client clinical trial data sets. Using our tools, the candidate analyzes and mitigates the risk of re-identification for trial participants. The ideal candidate learns quickly and is familiar with clinical trial data content and structures (CDISC SDTM & ADaM formats) and medical / healthcare language. They also have basic code literacyunderstanding of simple control flow (if/else and loops)adequate for working with SQL (including cursors and CTEs) and running Python scripts from the command line. These skills are complemented by strong technical acumen and the ability to meet tight deadlines.
Responsibilities
Develop familiarity with our anonymization methodology and the use of our tools/software suite.
Own assigned projects or project tasks and deliver such on time and with the required quality, providing timely and transparent status updates and managing client expectations.
Process clients clinical trials data sets. Core responsibilities include:
- Pre-processing (loading and cleaning) data sets (i.e. tables)
- Understanding and classifying tables and variables content
- Identify and classify direct and indirect identifiers
- Measure and mitigate re-identification risk
- Determine and apply required data transformations
- Facilitate the creation of anonymization reports and other client deliverables
Collaborate closely with other team members, ensuring that all work meets established quality standards and is completed on time. Participate in peer-reviews of work products.
Identify and communicate tool enhancements/refinements and relate defect documentation to developers and researchers.
Participate in thought leadership and presentations, when requested.
Qualifications
Exceptional detail-orientation.
Working experience with any of SQL, Python, or SAS; willingness to learn new ETL programs.
Analysis of clinical or healthcare data is required; ability to understand and interpret datasets and variables in clinical trials.
Ability to learn our methodology and develop a thorough understanding of how directly and indirectly identifying information contributes to the risk of re-identification.
- This is generally demonstrated through experience and comfort with scientific and quantitative disciplines; for example, clinical research, STEM fields, statistics, etc.
Well-developed communication and interpersonal skills.
The drive to meet exact deadlines and the flexibility to adapt to shifting priorities.
Ability to manage sensitive information with the utmost confidentiality.
1 to 3 years of professional experience preferably working with pharmaceutical data or in data and/or document anonymization.
Would have a degree in Statistics, Health Sciences, Biomedical Sciences, or closely related fields.
Experience with MS Office products (Excel, Word, and PowerPoint) and Adobe PDF.
Nice To Have / Beneficial Assets
CDISC SDTM / ADaM working knowledge.
Clinical trial programming experience.
Advanced Python development experience.
