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Senior Scientist (Statistical Genetics)

Jobot Raleigh, NC

  • Expired: March 12, 2021. Applications are no longer accepted.

This Jobot Job is hosted by: Emily Olinger
Are you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume.
Salary: $130,000 - $180,000 per year

A bit about us:

We are a leading cloud based SaaS startup in the biotech industry. Our platforms leads to insights for improved diagnostics, targeted therapies, and better patient care. We recently completed a $100 million financing round to advance our growth globally to further serve leading healthcare and life science organizations! If you are interested in becoming a leader in the Biotech space, apply today!

Location: Mountain View, CA or FULLY REMOTE

Why join us?

  • 100% of health premiums paid by employer for YOU, 75% paid for dependents
  • FSA
  • 401K and Stock options
  • PTO/Vacations
  • Great company culture
  • Remote options
  • Rapidly growing company

Job Details

  • Design and development of computational methods and tools for large scale data analysis and visualization in support of translational genomics research
  • Engage with customers to understand the research and analytics goals for the end users in pharmaceutical and academic research settings
  • Define solutions that meet customer requirements and research goals, working closely with program management, data science and engineering teams to drive those solutions through development and customer validation in an agile environment
  • Conceptualize and develop optimal methods/pipelines including Jupyter Notebooks and scripts for a diverse set of genomic data analysis workflows that allow customer scientists to gain insights from large scale data.

Skills and Requirements:
  • Ph.D in Statistical Genetics, Genetic Epidemiology, Bioinformatics, computational biology, computer science or related biotechnology field
  • Experience in bioinformatics, biostatistics, genomics, statistical genetics, population genetics, systems biology, and/or translational research
  • Statistical genetics methods and tools including GWAS (PLINK, HAIL, BOLT-LMM, SAIGE, RVtests, SKAT, METAL), PheWAS (PLATO, PHESANT), Polygenic Risk Score analysis (PRS), Mendelian randomization, fine mapping, pathway analysis
  • Understanding of existing techniques for managing and analyzing genomic, clinical/phenotypic, pharmacokinetic, and other molecular data (transcriptomic, metabolomic, proteomic, microbiome), and the challenges in aggregating datasets for reuse in follow on studies
  • Large scale omics datasets (ENCODE, 1000 Genomes, ExAC/gnomAD, TCGA
  • Python

  • Bash

Bonus Points!
  • Cloud computing, high performing computing
  • Big Data (Spark, Hive, Hadoop)
  • Integrated tools such as GDC DAVE, cBioPortal, i2b2 tranSMART, UCSC Genome Browser, Ingenuity Pathway Analysis
  • Reference and annotation databases (OMIM, ClinVar, gnomeAD), Multi-omic QTL databases (GTEx, eQTLgen, SPANR)
  • Experience with cohort design based on clinical datasets such as the UK Biobank

Interested in hearing more? Easy Apply now by clicking the "Apply Now" button.


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