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Palo Alto, California, United States
Hybrid
The Senior Data Scientist will collaborate with a cross-functional team of laboratory scientists, clinicians, engineers, and other data scientists to develop statistical analysis methods supporting early cancer detection assays. This role spans assay development, analytical validation, and QC and control strategy, contributing to products advancing through verification, validation, and regulatory submission toward clinical use. The ideal candidate brings strong foundations in probability theory and statistics, applied experience in a regulated diagnostics or similarly rigorous data environment, and the ability to translate complex analyses into clear documentation for cross-functional and regulatory audiences.
Design and execute analyses supporting assay development, analytical validation, and QC and control strategy, applying statistical methods appropriate to regulated diagnostic development
Collaborate cross-functionally with assay development, lab operations, and software teams to design experiments, interpret results, and produce documentation meeting quality system and regulatory standards.
Contribute statistical and data science expertise to control strategy development, including qualification and monitoring frameworks for reagents, instruments, and sample level QC.
Provide operational data science support for production and LDT/IVD run monitoring, including troubleshooting assay performance issues, trending QC metrics, and supporting root cause investigations.
Produce high quality Python code following best practices established by the software development team.
Support regulatory submissions (CLIA/CAP, NYS, FDA as applicable) with rigorous, well documented analysis.
Required
Master's degree in Applied Math, Statistics, Bioinformatics, Engineering, or a related quantitative field; PhD preferred.
Five or more years of practical experience in scientific or mathematical data analysis, with at least three years in Python (numpy, pandas, and similar).
Strong foundations in probability theory and statistics, including hypothesis testing, confidence intervals, parameter estimation, and variance components.
Experience working in a regulated diagnostics environment (IVD, LDT, or similar), including designing and executing rigorous studies and analyses and documenting them appropriately.
Ability to connect assay chemistry to data behavior, applying this understanding to troubleshoot assay performance issues.
Preferred
Exposure to NGS based assay development, including familiarity with sequencing data QC and batch level variability.
Experience analyzing high dimensional genomic or molecular data.
Familiarity with common machine learning techniques and practical experience with implementation and validation.
Familiarity with Design of Experiments and related quality methodologies (measurement systems analysis, control charts, Six Sigma/DMAIC).