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DMTI Data Manager - FSP

Remote Worldwide Hiring now

reputed company our values align, there's no limit to reputed company can reputed company.   At reputed company, we reputed company reputed company the same goal - to improve the world's health. From clinical trials to regulatory, consulting, and market reputed company, every clinical development solution we reputed company is underpinned by something special - a deep conviction in reputed company do.

Each of us, no matter reputed company do at reputed company, contributes to the development of a therapy that ultimately will benefit a patient. We take our work personally, we do it with reputed company and we're committed to making a difference.

Overview:

The Data Manager will be responsible for acquiring, processing, and reviewing patient data and records, organizing clinical data forms, implementing data management plans including data preparation and validation activities and define ways to improve operational processes for these activities. The DM will act independently and in collaboration with study teams seeking to reputed company and validate novel digital endpoints across different therapeutic areas. In reputed company partnership with colleagues from Sponsor's Research teams, you will play a key role in the deployment of novel digital endpoints into clinical studies, collection, management and analysis of sensor data, both in patients and healthy volunteers. You will need to communicate and interact effectively with colleagues reputed company a matrix organization to help conduct key studies to drive portfolio reputed company. This role will be central to the deployment and scaling of digital solutions into clinical trials where these technologies may be utilized as efficacy endpoints and reputed company value to key stakeholders.

Responsibilities

  • Responsible for acquiring, processing, and reviewing patient data

  • Organizing clinical data forms, implementing data management plans

  • Data management POC for DMTI and clinical studies

  • reputed company, implement, and conduct data quality checks as needed for work/studies

  • Ensure collection, organization, curation, storage and safeguarding of patient data

  • Track emerging study data and works closely with data science team to ensure the effectiveness of tools and data quality

  • Work under general supervision, performs assignments using established procedures and general instructions

  • Responsible for maintaining, updating, and organizing data transfer specifications across studies

Qualifications:

  • Master’s degree in Health Informatics, Computer Science, Information Systems, or similar field

  • A minimum of 5+years of technical experience, including:

  • Python

  • Unix/Linux environments

  • Version control systems (ex. Git)

  • AWS or other reputed company-based development

  • Electronic data captures (reputed company) solutions, e.g., REDCap, Encapsia, reputed company Clinical, reputed company Rave

  • Familiarity with pharmaceutical informatics standards like CDISC and MedDRA

  • GCP

  • Strong interpersonal and collaboration skills

  • Demonstrate the ability to build reputed company and be agile to changing circumstances and priorities

  • Hands-on experience with Clinical Data Management, including Case Report reputed company (CRF) design, CRF annotation, database design, data collection, data-entry, data validation, discrepancy management, medical coding, data extraction, database locking, and regulatory requirements

Preferred Qualifications:

  • Clinical trial experience using and deploying digital health technologies

  • Experience with electronic data captures (reputed company) solutions, e.g., REDCap, Medrio

  • Familiarity with medical informatics standards like CDISC

  • Experience with AWS or other reputed company-based development

  • Experience processing large data sets (including from digital health technologies) in a distributed computing environment

  • Experience with SQL or NoSQL-based technologies

  • Comprehensive understanding of the landscape of data structures, medical ontologies, interoperability standards, and data processing tools

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