Join the Recombine Team as a Bioinformatics Scientist (New York, NY)
Want to be a part of clinical and translational research studies that could lead to the development of new and innovative genetic testing products? Are you process-oriented and take pride in your analyses? Are you comfortable working both independently and in collaboration with others developing quantitative models and algorithms? If yes, come work with us as a Bioinformatics Scientist!
What is Recombine?
Recombine seeks to develop the most comprehensive and cost-effective patient-and-doctor friendly approach to genetic testing. Our product, CarrierMap, helps identify couples at risk of passing a genetic disease to their children by testing before pregnancy. Our company was founded by experts in fertility, clinical genetics, bioinformatics and computer science brought together with one goal in mind: to improve health outcomes based on accessible and actionable genetic testing.
Why Work at Recombine?
- Above all, you will be working on a team with some of the most passionate and intelligent scientists in the fields of fertility and genetics.
- Our office located in the heart of the Flatiron District in New York City. You will be a part of an enthusiastic and collaborative team that is focused on bringing genetic testing to a wide audience.
- We find the right people to help us find the best solution to every problem. In your position, you will have the opportunity to help grow Recombine and build the culture that will revolutionize genetics.
- We have partnered with some of the top IVF Clinics in the country. Our team and advisors are routinely recognized for their work in reproductive genetics. We work with and employ many of the luminaries in this field.
- We love to solve difficult problems, and we are constantly looking to innovate. This culture of continual innovation is one of the greatest fulfillments of the job.
What Role Will I Fill?
- 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
What Qualifications, Skills & Abilities Should I Have?
- 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
Meet Some of Recombine's Employees
Chief Technology Officer
Paul leads an agile team of engineers and data analysts to develop Recombine’s platforms and engineering testing grounds, including lab automation and using compelling technologies to analyze raw data.
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