Data Anonymization Analyst

Summary

The Data Anonymization Analyst is part of the data42 Data Anonymization team and is responsible for anonymization of data sets for data42. A key aspect of the program is to anonymize the data to enable secondary research on data lakes. The position is a key collaborator with other data42 teams. Ensure that data sets are anonymized efficiently with timely and high-quality deliverables for data42 users. This individual will be responsible for anonymizing data from clinical trials, including clinical raw and derived data of the clinical database (RDC, lab and 3rd party data), images, documents and other unstructured data. Use software products to anonymize data sets against, and to mitigate, the risk of re-identification of personal data.  Identify and classify direct and indirect identifiers. Create reports. Support efficiency improvements in the field of data anonymization. Manages and advocates data quality, as being the most downstream teams, before data is being provisioned to the end-users.

About The Role

Major Activities:

• Perform Data Anonymization activities for data42.
• Collaborate with other data Anonymization analysts either internally or externally assigned to the project. Make anonymization decisions/recommendations as required.
• Communication with stakeholders, ensuring data anonymization and reporting meets quality requirements and on-time delivery.
• Build and maintain effective working relationships with cross-functional teams, able to summarize and discuss the status of deliverables.
• Comply with company, department and industry standards (e.g. CDISC) and processes, assess and clarify additional requirements at project level in liaison with the functional leaderships.
• Ensure timely and quality anonymization and validation of datasets and outputs
• Responsible for quality control and audit readiness of all assigned anonymization deliverables.
• Maintain up-to-date knowledge of programming software (e.g. SAS) as well as industry requirements (e.g. CDISC SDTM/ADaM, eCTD, Define.xml), attend functional meetings and training.
• Understanding and ideating around the Risk-based anonymization strategy is one of the key advantage one carries- a unique way of anonymizing data statistically.
  
  

Key Performance Indicators

• Achievement of goals with high quality and timeliness of anonymization deliverables and contributions as assessed by the Anonymization Product Owner, internal and external customers.
• Collaboration with other data42 product teams
• Ability and effectiveness in execution of responsibilities.
  
  

Impact On The Organization

• Successful delivery of assigned projects: Timely, high quality and efficiently produced anonymization output
• Innovative solutions and improvements to support timely and efficient anonymization deliverables
• High quality and impactful work product to support the data42 data anonymization product team
  
  

Minimum Requirements

Education (minimum/desirable): BA/BS/MS or international equivalent experience in statistics(preferred), computer science, mathematics, life sciences (especially Genetics/Bioinformatics) or related field

Experience

• 2-3 years in a programming role managing end to end anonymization related or other related experience within the pharmaceutical, healthcare, supporting clinical trials/ or in pharmaceutical industry
• SAS experience and proven skills in the use of SAS
• Executing and running commercially available anonymization applications (preferably following the Risk-based anonymization methodology- a statistical approach to anonymization)
• Good knowledge of industry standards including CDISC data structures as well as a solid understanding of the development and use of standard programs
• Understanding of Python language would be an additional advantage, with knowledge around Databricks alike platforms fx. Palantir’s Foundry platforms and pipelines
• Open to apply artificial intelligence/LLMs approaches to anonymizing data and autonomous ways of working
• Good understanding of regulatory requirements relevant to Statistical Programming (e.g. GCP, study procedures).
• Passionate and deeply knowledgeable about data management, data anonymization and clinical data structures
• Good communications and negotiation skills, ability to work well with others globally
• Established project management skills with a proven track record of leading and/or collaborating with teams in complex global matrix structures and business models.
• Collaboration / teamwork / agile methodology
• Attention to detail.
  
  

Languages

Fluent English (oral and written).

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