Bioinformatics Scientist

Recombine is here to simplify genetic testing for you. We manage all aspects of genetic testing, from sample collection to genetic counseling. Our goal is to ensure genetic information is delivered in an accurate, informative and responsible manner.

• Extract actionable insight from large genomic datasets and contribute to the launch of new products
• Collaborate with the bioinformatics and clinical study teams to integrate genomic information with medical history.
• Develop machine learning and data-driven analyses to investigate the association between disease phenotypes and genetic variants from our microarray genotyping and NGS sequencing pipelines
• Implement and develop statistical tools for analysis of population genetics and clinical study design data
• Assist in maintaining our databases and curating disease models.
• Participate in variant interpretation and phenotype analysis, assist with preparation of manuscripts, and guide clinical study design and data collection implementation.
• Prepare reports and presentations for clinical partners and the medical/scientific community on study progress, results and next steps

• Advanced degree (Ph.D. preferred) in Bioinformatics, Biostatistics, Statistics, Math, Computer Sciences or another quantitative discipline
• Previous experience integrating human genomic and disease phenotypic data to predict associations between genetic variants and disease diagnoses
• Working knowledge of microarray genotyping and sequencing pipelines
• Fluent in modern advanced analytical tools and programming languages (e.g. Python, R, Ruby, etc.), databases (SQL) and experienced in cloud-based systems.
• Previous practical experience implementing statistical principles and machine learning algorithms to large genomic datasets
• Curious and pragmatic. We want you to be excited about the project and able to ask the right questions
• Comfortable with problem-solving and the ability to identify and resolve issues quickly
• Strong verbal communication skills and ability to discuss data, modeling, and analytics with technical and non-technical audiences