PRR Project

Summary

Data managers and data scientists/biostatisticians offer organizations, particularly those that heavily rely on data, such as research, a holistic approach to data exploitation and utilization. By leveraging the complementary skills and expertise of both profiles, organizations can optimize their data workflow, mitigate risks, foster collaboration, and drive innovation, ultimately gaining a competitive edge in today´s data-driven world.Clinical data management is pivotal in ensuring the accuracy of the data collection, quality assurance, and storage processes, thereby minimizing errors, lack of harmonization, missing entries, and data loss. Thus, the Data Manager is instrumental in overseeing the implementation of best practices in data handling, to generate robust evidence through reliable, accurate, and high-quality data suitable for statistical analysis and exploitation. Investing in robust CDM practices and infrastructure is essential for advancing medical knowledge, improving patient outcomes, and delivering high-quality research. On the other hand, data scientists/biostatisticians bring advanced statistical skills, machine learning expertise, and domain knowledge to the research center, by deriving insights and patterns from complex data.  To do so, statistical and machine learning techniques are indispensable for capturing biomarker profiles and integrating them into risk-predictive models that can effectively capture the complex nature of cardiovascular diseases. These data conflation techniques will enhance robust risk prediction modeling, thereby facilitating personalized medicine strategies and targeted disease prevention. By employing such advanced statistical methods, researchers gain deeper insights into the complex mechanisms underlying cardiovascular diseases, improve risk prediction models, evaluate treatment efficacy, and inform clinical decision-making for improved patient healthcare.The researcher appointed for this role will be asked to:Develop and implement appropriate protocols for data management, handling, and storage.Continuously monitor and evaluate data organization processes to identify areas for improvement.Create data management protocols and standards specific to this center´s clinical and basic research needs.Oversee data access proposals and requests for data sharing.Implement advanced statistical analyses (Multivariate Regression Analysis, Survival Analysis, Longitudinal Data Analysis, Meta-analysis, Machine-learning, Validation techniques).Manage complex clinical, molecular, and imaging datasets to identify disease biomarkers, including Big Data such as Omics data (metabolomics, lipidomics, proteomics, radiomics).Lecture in Master´s and Doctoral programs.Provide tutoring and mentoring of Master´s and Doctorate students.The desired scientific profile for the researcher includes:Demonstrated capabilities in analyzing complex data patterns, particularly those involving clinical, molecular, and imaging features.Proven experience in fusing clinical, molecular, and imaging data to uncover associations between biomarkers and clinical features and trajectories.Expertise in developing cardiovascular risk prediction models.Expertise in applying advanced statistical methods in clinical research.Experience in cardiovascular research.Proficiency in R programming.Established track record in implementing data management pipelines to ensure data harmonization, standardization, handling, and storage.The rationale for hiring a researcher with a Data Manager with Data Science/Biostatistics skills is that the researcher will be responsible and autonomous in covering the entire data lifecycle, from data collection to analysis, streamlining, and optimizing the overall data workflow. As organizations grow and data volumes increase, having a dedicated data manager and data scientist/biostatistician allows for scalability and flexibility. This synergy between data management and analysis drives innovation and will enable the research center to stay ahead in the rapidly evolving data landscape.