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 the data anonymization and reporting meets quality requirements and on-time delivery.
• Build and maintain effective working relationship with cross-functional teams, able to summarize and discuss 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 trainings.
• 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:

• 4-5 years in a programming role managing end to end anonymization related or other related experience within 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, 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 communication 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).