Who we are:
Calico is a research and development company whose mission is to harness advanced technologies to increase our understanding of the biology that controls lifespan. We will use that knowledge to devise interventions that enable people to lead longer and healthier lives. Executing on this mission will require an unprecedented level of interdisciplinary effort and a long-term focus for which funding is already in place.
Calico is seeking a Principal Data Scientist to join our efforts in advancing novel therapies from target discovery to clinical development. The ideal candidate, working collaboratively with the translational and development teams, will apply their deep technical knowledge in computational modeling of biological networks to help drive drug development decisions. The principal scientist will lead efforts to integrate aging mechanisms and diverse experimental datasets into a computational model that can be used to design experiments and simulate therapeutic strategies. The candidate will also be responsible for the analysis and interpretation of data in support of translational activities. This includes analysis of NGS and proteomic datasets, mining publicly available data sources, and supporting the identification of predictive and pharmacodynamic biomarkers. This role requires the ability to communicate computational results and outcomes to scientists in both quantitative and non-quantitative disciplines, as well as to external collaborators at AbbVie, the Broad, and others.
As a member of the Calico’s Computing team, you will work closely with a cross-functional group of computational biologists, data scientists, translational project leads, scientists, and biostatisticians to advance Calico’s pipeline. This role offers a unique opportunity to innovate at the leading edge of data science and quantitative systems pharmacology.
- Develop computational network models that integrate disease mechanisms. Use model simulations to explore and prioritize different therapeutic strategies
- Collaborate with other data scientists and engineers to develop rigorous data-driven methods to support translational drug development
- Explore high-dimensional phenotypic data (genomics, transcriptomics, metabolomics, proteomics, and imaging, across multiple experimental conditions) to better understand aging & disease biology and how these pathologies interact with our developing therapeutics
- Assist biomarker teams to develop and quantify prognostics, pharmacodynamics, and predictive biomarkers and/ biomarker signatures
- Communicate clearly and effectively in verbal, visual, and written form to stakeholders with varying levels of technical knowledge
- Ph.D. in a quantitative field such as Systems Pharmacology, Bioinformatics, Computational Biology, Statistics, Biological Sciences, Genomics, Computer Science, or equivalent preparation and experience
- Published expert in building computational models of biological networks
- Knowledgeable in all areas of model building: mathematical modeling, parameter estimation, and design of validation experiments
- 3+ years of work experience in the pharmaceutical, biotech or healthcare industry in modeling and simulation
- Experience with testing statistical hypotheses using high-dimensional phenotypic data (e.g., bulk RNAseq, single-cell RNAseq, ribosome profiling, ATACseq/ChipSeq, metabolomics, lipidomics, proteomics, microscopy, cell painting)
- Fluent in technical scripting languages such as Python, R, and/or MATLAB with experience using industrial best practice (version control, unit testing, and package management)
- Track record of effective cross-functional collaboration