Data are converted into the OMOP Common Data Model, making queries and analytics interoperable and sharable. In addition, the generation of these queries and tools and its execution can be separated, both physically as well as logically, creating the opportunity to develop code for purposes of descriptive statistics or hypothesis testing in the absence of a direct data access to all data assets being targeted. As a consequence, our team can generate insights across multiple datasets in our collaborators network.
The Data Standardization & Analytics team's mission is to deliver world class and globally scalable projects through:
Rapid analytics - to assess study feasibility and availability of data
Characterization of patient populations: their demographics, the distribution of their comorbidities, the duration between diagnosis and intervention, treatment patterns etc.
Network studies co-ordinating the execution of RWE analytics across multiple external data partner sites.
This requires global leadership across technical and data architecture, data manipulation, analytics script and report generation. The team also curates the largest collection de-identified Real-World Data in the world in OMOP Common Data Model, from different patient care settings in multiple countries worldwide, making it the forefront of Big Data in healthcare.
Design & develop analytical packages in R & SQL to extract real-world evidence from OMOP healthcare databases.
Executing retrospective analytical packages as part of OHDSI and other OMOP-based network initiatives.
Work collaboratively with OMOP data scientists, plus other team members across DSAE.
Support the Senior Data Scientists and other technical experts with building the teams subject matter expertise with regards to the OMOP common data model.
Collaborate with members of the OHDSI community, participate in the OHDSI community through study projects such as study-a-thons, code development and knowledge sharing
Support customer focused training sessions and tutorials where necessary
R programming (minimum 18 months)
Analytical / quantitative research background (preference for big data)
Masters or PhD in a relevant quantitative field.
SQL programming.
AI /ML experience.
Location and travel
Minimal travel expected to other offices (London, East Coast US) or to attend conferences and workshops as required.
Home-based