R&D data sets Partner with I&T to design and maintain scalable, secure, and interoperable data platforms Enable integration of diverse data types including clinical, real-world data, omics, imaging, and digital biomarkers Collaborate with Digital Business Partners and Analytics teams to ensure data readiness and accessibility for advanced analytics Foster cross-functional data alignment across clinical, safety, regulatory More ❯
R&D data sets Partner with I&T to design and maintain scalable, secure, and interoperable data platforms Enable integration of diverse data types including clinical, real-world data, omics, imaging, and digital biomarkers Collaborate with Digital Business Partners and Analytics teams to ensure data readiness and accessibility for advanced analytics Foster cross-functional data alignment across clinical, safety, regulatory More ❯
driven insights using analytics and machine learning on high-dimensional datasets conducted with complex biological assays (e.g. next-generation sequencing, cell-based assays, time-series assays, high-content assays, -omics assays, etc.). Clear communication of outputs & results from analyses for all relevant stakeholders. Adapt and design data processes to support the capture and analysis of assay data and metadata More ❯
unique collection of data and predictions in real-time. Automation of end-to-end data flows: Faster and reliable ingestion of high throughput data in genetics, genomics and multi-omics, to extract value of investments in new technology (instrument to analysis-ready data in ?12h) .Enabling governance by design of external and internal data: with engineered practical solutions for controlled More ❯
unique collection of data and predictions in real-time. Automation of end-to-end data flows: Faster and reliable ingestion of high throughput data in genetics, genomics and multi-omics, to extract value of investments in new technology (instrument to analysis-ready data in ?12h) .Enabling governance by design of external and internal data: with engineered practical solutions for controlled More ❯
and computational scientists across Altos. Influence best practices in areas such as Bayesian optimization, causal inference, building and assessing predictive models, analyzing biological networks, and analysis and visualization of -omics data. Maintain documentation and keep up-to-date as needed. Contribute to software releases (e.g. via GitHub, PyPI, Anaconda, Docker Hub). Who You Are The ideal candidate will be More ❯
