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The CM Principal Analyst (CMPA) is a key member of Centralized Monitoring (CM) responsible for designing and implementing novel data analyses to address emerging business needs and bring innovations into CM and Risk based Quality Management (RbQM).
CMPA will collaborate closely with Centralised Monitoring Analysts (CMA), CM Therapeutic Area Leads (CMTAL) and other functions outside of the CM to understand business needs and developments in corporate analytical environment to design, develop and implement new data analyses using advanced analytical methods to support data driven decisions in quality control of clinical trials.
This role is located within Centralized Monitoring, Development Operations, R&D Pharmaceuticals Unit reports to Associate Director Centralized Monitoring.
CM Principal Analyst will :
Contribute to identification and selection of areas to be addressed by new CM functionalities.
Design, develop and implement new analyses of data from ongoing and historical clinical trials, including but not limited to : Quality Tolerance Limits historical data analysesSupporting Root Cause Analyses (RCA) in Issue ResolutionPredictive analyses to model data trends and identify potential risk areas which may lead to defining new Risk Indicators (RI)
Support CMTALs and CMAs in choosing proper approaches to new types of study specific data analyses
Design new standardised and study specific CM functionalities (CM dashboards and monitoring action requests) to ensure they meet business requirements that emerge from Critical-
to-Quality (CtQ) standards and Integrated Quality Risk Management Plans (IQRMP).
Understand software development lifecycle and have up to date knowledge about associated SOPs to be able to implement its principles in designing and prototyping new CM functionalities
Explore and leverage the opportunities to apply advanced analytic techniques, e.g. data mining, forecasting, simulation, machine learning and natural language processing.
Provide consultancy and training in area of data analysis to other CM team members, including training on the novel CM functionalities.
Create prototypes, demonstrations to help others better understand complex analytics and proposed solutions.
Develop and maintain strong working relationship with Data Science & AI experts across the organization.
Maintain a keen awareness of developments in a field of data science, especially its applications in clinical trials.
Act as a Mentor to provide guidance and support for other CMAs
May be assigned as a primary CMA for a clinical trial to execute centralized monitoring services as per the IQRMP.
MSc Degree or equivalent in the life sciences or quantitative fields (e.g. computer science, statistics, mathematics)
Excellent knowledge of Risk-Based Monitoring (RBM) methodology and tools, industry trends, and role and place of CM within RBM
Thorough practical knowledge of R environment
At least 4years of experience in analysis of data in clinical trials
A good understanding of drug development processes including different monitoring practices and regulations, GCP and quality requirements in clinical trials
Previous work experience in RBM model and CM
Excellent communication and influencing skills
Demonstrated ability to set and manage priorities, performance targets and project initiatives in a global environment
Ability to interact with Senior Management
Cultural Awareness : Is aware of and sensitive to cultural differences and their impact on communication, expectations and performance
Experience working internationally : working with major markets / resolution of international issues
Teamwork and cross functional Collaboration : Is effective in leading and being a member of teams, both locally and internationally.
Good spoken and written English
Knowledge of other than R programming languages used for data science, e.g. Python or SAS, is an advantage
Evidence of data science training is an advantage
Previous experience in application of advanced analytics in a field of clinical trials is an advantage.
Evidence of using analytical thinking to influence practice would be an advantage
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