* 국제백신연구소는 대한민국에 본부를 둔 국제기구로 세계 각국에 지사를 설립하여 운영되고 있으며, 세계 공중보건을 위한 안전하고 효과적이고 저렴한 백신의 발굴, 개발 및 보급의 사명을 다하는 국제기구입니다.


국제백신연구소 Data Science & Innovation / Data Management 부서에서 Associate Researcher 를 모집합니다.


[채용 기관 및 부서]

• 기관 : 국제백신연구소

• 부서 : Data Science & Innovation / Data Management


[주요 업무]

1. Support the development and implementation of the data management (DM) systems


- Support the development of data collection and management system

- Maintain quality of the data management system according to the requests or new technical knowledge/skill

- Transfer solutions of the DM system to study site

- Maintain DM system with safety, security, and confidentiality

 


2. Data management on study site


- Regular monitor the data flow and relevant activities in study sites

- Maintain data integrity and completeness

- Support in development of training materials used in study site for DM systems and data activities

- Conduct DM training on study site to data managers and data entry users

- Communicate closely with data manager of study site for data flow done appropriately

 


3. Execute the Data reporting


- Monthly Report to investigators and supervisor on the progress and performance of the data flow and activities of study sites

- Support to write annual report and ad-hoc reports as necessary

- Provide TFL (table, figure, lists) of acquired data based on the statistical analysis plan (SAP) or investigator’s requests

- Convey statistical outcomes if requested


[자격 요건]

• 최종 학력 : 석사 이상 (Master’s degree or equivalent with experience) 

• 경력 : Master’s degree: 3+ years of experience in Data Management or Statistics


[근무 조건]

• 근무 시간 : 주 40시간 (오전 9시 ~ 오후 6시)

• 근무 형태 : 정규직 (2years/renewable)


[채용 절차]

• 서류 전형 및 면접 전형 : 면접 일정은 서류 합격자에 한해 개별 연락 예정

• 제출 서류 : 영문이력서 

• 지원 방법 : 홈페이지 접수 (https://www.ivi.int/associate-researcher-dsi_data-management-department-based-in-seoul/)

• 지원 기간 : 채용 마감 시까지


 welcomes applications from all sections of the community. We encourage women to apply, especially from countries that are currently under-represented within.