Senior Scientist, Statistics

South San Francisco, CA, United States
Oct 18, 2020
Biotech Bay
Required Education
Position Type
Full time

A successful Senior Scientist of Statistics will work closely with scientists, clinicians, Technical Operations engineers, and computational biologists to analyze a wide range of data and develop bioinformatics and statistical methods to solve exciting and challenging problems in cell and gene therapy, immunology, and cell biology. This person should be a team player and fantastic at cross-functional communication.

The ideal candidate should be excited about analyzing NGS data, designing and employing meta-analyses across data types, and developing and applying modern statistical methods for complex data sets, and being able to quickly prototype solutions. The candidate should have a strong technical background in NGS data analysis including the algorithms underpinning foundational tools and the current statistical frameworks utilized for analysis. This candidate should be able to partner effectively with teams across Sana including Research, PharmTox, Translational Science/Clinical, and Technical Operations to statistically consult on study designs, modeling, and analyses. The ability to be flexible, work in a highly collaborative environment and have an agile team-first mindset will be critical to success.

  • Develop analysis workflows from existing algorithms or modify existing methods to analyze NGS and other types of data in Research, Analytical Genomics, Translational Science/Clinical, and Technical Operations. This includes development of novel Bioinformatics methods as needed to answer platform and biological questions to meet company objectives. Demonstrate validity of methods through prototypes and benchmarking using appropriate controls
  • Partner with internal stakeholders to design and analyze studies that are appropriately powered with the proper controls to meet experimental and business objectives
  • Provide organizational expertise in statistics ranging from foundational principles of experimental design (e.g. DOE) and data analysis to complex methods of data modeling and applied statistics in areas such as analytical assays, translational science, biomarker discovery, and manufacturing
  • Identify, analyze, and integrate public and internal datasets (e.g. NGS, single cell, genetics) within the provided Sana infrastructure to facilitate a deeper understanding of research initiatives, platform development and disease pathology
  • Proper documentation of code, workflows, and analyses including study reports for nonclinical and clinical studies
  • Work with the Computational Biology and Engineering teams to review and test code, and refactor prototype workflows to production grade as necessary
  • Communicate to a broad audience with a range of technical, analytical, and biological expertise and present results on a regular basis at various group meetings


Required Qualifications
  • PhD with 5+ years academic/industry experience in statistics, data science or related discipline
  • 3+ years of hands-on experience with all steps of large-scale DNA- and RNA-Seq data analysis (FASTQ through variant calling and/or differential expression including QC)
  • Proven experience in applying statistical modelling and machine learning concepts for complex quantitative data analysis, protein/DNA/RNA sequence analysis at low or high-throughput scale
  • Experience with integration of and statistical methods for mining ‘omics data (transcriptomics, genomics, epigenetics, etc.)
  • Demonstrated proficiency in R
  • Experience with high performance computing (local or cloud) and proficiency in *ix
  • Routine use of version control (e.g. git, svn)
  • Demonstrated strong problem solving abilities and organizational skills
  • Must be detail oriented, self-motivated, flexible, and able to prioritize and manage several fast-paced projects concurrently
  • Outstanding verbal and written communication skills for technical and non-technical audiences
  • Demonstrated ability to work in cross-functional teams as a strong team player as well as independently

Preferred Qualifications
  • Proficient in Python
  • Prior experience with analytical assay development is a significant plus
  • At least 1 year of experience with application of innovative quantitative methods in gene or cell therapy setting preferred
  • Experience with documentation tools and software best practices in Python and R
  • Familiarity with workflow languages (e.g. Nextflow, Snakemake)