We have an exclusive need for a Data Quality Lead Role
Must have Clinical Data experience.
100% Remote
Data Quality Lead (Contract Position)
Join client’s US Medical Data Management Team as a Data Quality Lead to pioneer data life-cycle management. This vital role involves acquiring and curating data for research, clinical development, and evidence generation.
If you are strategic, collaborative, and entrepreneurial, you will thrive by designing and implementing cutting-edge, fit-for-purpose data management solutions that adhere to F.A.I.R. principles.
You will ensure timely completion of deliverables, partner with FSPs, CROs, and vendors to provide technical oversight, guarantee high-quality data, and ensure regulatory compliance. Your contributions will shape our technical landscape and the broader healthcare environment through internal and external initiatives.
The Opportunity
The Data Quality Lead will manage all study life-cycle activities across various complex studies. In addition to demonstrating high proficiency with core data management tasks (working independently with minimum oversight), this role provides leadership to data management team members and adapts strategies to meet changing program objectives.
- Develop risk management strategies, proactively manage timelines, and ensure successful oversight and delivery of studies & projects.
- Partner with relevant functions for external data vendor selection and management, overseeing the development of data transfer agreements to ensure use of standards and fit-for-purpose models.
- Act as an expert, advising teams and stakeholders on best practices and proposing innovative solutions for data collection, including new data types (e.g., emergent biomarkers), technologies (e.g., sensors), and sources (e.g., EMR).
- Provide quality assurance for all deliverables, ensuring a high quality of data and compliance with applicable pharma industry regulations and standards (ICH-Google Cloud Platform).
- Conduct high-level review of data using visualizations and statistical monitoring tools to identify outliers or anomalies.
- Organize and integrate data, maintaining its value through the application of F.A.I.R. principles.
- Proactively engage with stakeholders across the business to understand their needs, influence their understanding of function decisions, and offer Data Management solutions.
- Offer guidance and advice to peers, key stakeholders, FSPs, CROs, and collaborative groups on technical solutions to ensure high-quality data collection and delivery.
Who you are
- Bachelor''s or Master''s degree in a relevant field such as computer science, life sciences, statistics, or informatics, or equivalent professional experience.
- A minimum of 5 years of experience in clinical data management. Candidates with an advanced degree require a minimum of 3 years of equivalent work experience.
- Demonstrated, practical expertise in clinical trial data management, encompassing proficiency in Case Report Form (CRF) design and query resolution.
- Expertise in leading data management study teams and managing all study life-cycle activities, from start-up to close-out, across various complex studies.
- Working knowledge of Electronic Data Capture (EDC) systems and CDISC data standards.
- Experience working in a regulated environment, adhering to standards such as ICH-Google Cloud Platform.
- Solid understanding of various clinical data types (e.g., clinical, biomarker, WGS, RNA-seq) and their relationship to analysis, coupled with biological principles.
- Excellent written and oral English communication skills.
- Demonstrated leadership in decision-making, negotiation, motivation, and influencing.
- Ability to independently manage multiple, prioritized deliverables with strong project management.
- Familiarity with risk-based monitoring or data quality metrics.
- Proven understanding of data analytics and/or visualization.
- Inclusive mindset, embracing differences in colleague interactions.
Preferred
- Experience in leading the collection of Real World Data.
- Some experience with advanced analytics approaches (e.g., machine learning, AI).
- Programming literacy (e.g., SAS, R, Python) for data review or visualization.
- Exposure to data flow across the clinical systems landscape (e.g., EDC to SDTM).
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