As part of JRD Data Sciences team, you will lead, design and implement efficient data standards, data models as well as analytics solutions that enable the optimal implementation of science capabilities across R&D. We support projects from discovery through late development. You will lead and establish solutions that operationalizing analytics, mapping data requirements to implementation, evaluating/selecting appropriate solutions for analytics pipeline and procedures. The role requires a broad knowledge of existing biology/clinical data standards, analytics procedures and analytics solutions. You will be part of dynamic, accomplished team that supports multiple R&D therapeutic areas in the discovery and development and deliver data sciences solutions.
In this role you will:
- Lead and design data & analytics architect strategy for R&D Data Science community.
- Work closely with R&D Data Scientists and scientists across TAs/Functions to implement data standards/model to support R&D Data Science analytics initiative including but not limited to molecular data, clinical trial data and real-world data
- Design and deliver rigorous and sound analytics architecture to ensure optimal analytics lifecycle
- Lead and participate evaluation of Data Sciences tools and technologies
- Ph.D. with a minimum of 5 years relevant of experience (OR Master's degree with a minimum of 7 years of relevant experience) in Bioinformatics, Statistics, Computer Science, Information Technology, Operation Research or a related discipline
- Strong business insight, deep knowledge in drug discovery, clinical development or healthcare products commercialization processes
- Deep understanding and working knowledge in healthcare data standards such as CDISC SDTM, ADM, CDM, FHIR
- Deep knowledge in data model design and data integration with diverse data with track record in establishing framework for integrating diverse of data into understandable and actionable insights
- Excellent communication, interpersonal, and written skills
- Proficient with one or more programming language such as Python, R, or SAS
- Understanding of data science projects using machine learning, deep learning, data mining and/or text mining