Our big vision comes with big responsibility. That’s why we’re building a team of experts in the field of genetics, engineering, design, business development, and beyond to help bring actionable insights to our customers. We’re looking for the best and brightest minds who are passionate about our mission and are excited to work with a truly diverse team.
We are looking for a senior level scientist to be the first member of our Partner Research team. As the first member of this team you will manage and advance partner research collaborations that aim to develop new insights for partners and users. This work will involve the development and implementation of scalable algorithms for prediction across population-level genomic and phenotypic data. You will also work closely with our partner scientists, bioinformaticians, and engineers to develop these new insights into features that can be delivered to our customers. This role is ideal for a scientist trained in population genetics or genetic epidemiology, with a history of independent scientific inquiry, who enjoys team science and is interested in gaining cross-functional experience in a fast moving industry environment.
As Staff or Principal Scientist, you will:
- Keep up-to-date on the latest science in scientific areas related to large scale genotype-phenotype biomarker discovery and genetic prediction.
- Prioritize and manage Partner Research opportunities
- Work directly with lead scientists from Helix’s partners to ask key scientific questions and then perform novel analyses to test these hypotheses.
- Understand partner requirements to influence Helix’s technical roadmap.
- PhD and Post-doc in statistical genetics, epidemiology, or related field (genetics, computer science, public health, etc)
- Track record as an independent scientist with a research program focused on human population genetics, population health or disease genetics; able to ask and drive interesting scientific questions
- Experience analyzing and manipulating large datasets in R, Python, or SAS; C/C++ or Java
- 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
- Has developed algorithms and/or statistical learning approaches
- Strong communication skills, both oral and written. Must be able to explain their science to the scientific community as well as to non-scientists.
Ideal candidate will have:
- Tenure-track candidate or strong performance in a commercial organization.
- Experience with managing and participating in large-scale scientific consortiums.
- 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 with phenotype modeling
- Entrepreneurial spirit; willing to contribute in multiple areas