We are looking for a curious and engaged scientist to join our Bioinformatics team. As part of this team, you will research and develop novel algorithms for genomic prediction and population-scale analysis using public and internal data, including that from the largest CLIA-certified exome sequencing lab in the world. You will work closely with our partner scientists, bioinformaticians, and engineers to deploy new features for our partners so they can, in turn, deliver new insights to our customers about their DNA. You have an insatiable curiosity, entrepreneurial spirit, and are problem focused. This role is ideal for a population geneticist, epidemiologist, or biostatistician, with strong interdisciplinary scientific training and an interest in gaining cross-functional experience while maintaining expertise in several translational research areas.
As a Population Geneticist/Epidemiologist, you will:
- Keep up to date on the latest science in scientific areas related to oligogenic risk scores, large scale GWAS/ PheWAS & rare variant gene burden testing, and meta-analyses
- Evaluate and incorporate latest analytical approaches in statistical genetics in an industry environment
- Research and develop novel statistical and/or machine learning approaches to support product development with Helix’s partners
- Ensure that products on the Helix platform are based on scientific grounds
- PhD in statistical genetics, epidemiology, or related field (genetics, computer science, public health, etc)
- Evolutionary or human population genetics research experience
- Experience analyzing and manipulating large datasets in R, Python, or SAS; C/C++ or Java a plus
- Experience analyzing GWAS, PheWAS, and gene burden tests.
- Experience with LDpred, PLINK, PRSice, SKAT-O
- Strong communication skills, both oral and written. Must be able to explain their science to the scientific community as well as to non-scientists.
- Entrepreneurial spirit; willing to contribute in multiple areas
Ideal candidate will have:
- 2+ years post-doc or industry experience
- Understand the theoretical frameworks for GWAS, Genetic Risk Scores and Gene burden tests and be able to extend known tools into novel and scalable approaches
- Experience with Linux-based high-performance computing environments (on-prem or cloud platform(s))
- Up-to-date knowledge of and ability to use genomic annotation and interpretation tools.
- Experience in algorithm development and/or statistical learning approaches