Clinical Data Manager manages all aspects of clinical database design and reporting. Establishes and maintains policies and procedures for gathering, analyzing, and reporting clinical data. Being a Clinical Data Manager standardizes data management procedures and documents departmental operating procedures. May require an advanced degree. Additionally, Clinical Data Manager typically reports to a manager or head of a unit/department. The Clinical Data Manager typically manages through subordinate managers and professionals in larger groups of moderate complexity. Provides input to strategic decisions that affect the functional area of responsibility. May give input into developing the budget. To be a Clinical Data Manager typically requires 3+ years of managerial experience. Capable of resolving escalated issues arising from operations and requiring coordination with other departments. (Copyright 2024 Salary.com)
Position Title: Computational Data Scientist
Seeking a highly motivated and driven data scientist to join our Quantitative, Translational & ADME Sciences (QTAS) team in North Chicago, IL. The QTAS organization supports the discovery and early clinical pipeline through mechanistically investigating how drug molecules are absorbed, distributed, excreted, metabolized, and transported across the body to predict duration and intensity of exposure and pharmacological action of drug candidates in humans. Digital workflows, systems, IT infrastructure, and computational sciences are critical and growing components within the organization to help deliver vital results in the early pipeline. This specific job role is designed to act as an SME (subject matter expert) for data science within the technical organization of QTAS.
For this role, the successful candidate will have a substantial background in data and computer science with an emphasis on supporting, developing and implementing IT solutions for lab-based systems as well as utilizing computational methods. The candidate should possess a deep knowledge in AI/ML, with a focus on both supervised (like neural networks, decision trees) and unsupervised learning techniques (such as clustering, PCA). They must be adept at applying these methods to large datasets for predictive modeling; in this context- drug properties and discovery patterns in ADME datasets. Proficiency in model validation, optimization, and feature engineering is essential to ensure accuracy and robustness in predictions. The role requires effective collaboration with interdisciplinary teams to integrate AI insights into drug development processes. Strong communication skills are necessary to convey complex AI/ML concepts to a diverse audience.
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Additional Details :
Projected Start Date : 2024-05-13T00:00:00
Projected End Date : 2025-05-13T00:00:00
Client Company : AbbVie
Vendor Pay Rate : 60
Selling points for candidate : This is a high PRIORITY requisition. This is a PROACTIVE requisition
Face to face interview required : No
Candidate must be authorized to work without sponsorship : No
Background Check : No
Drug Screen : No
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