Our clients are seeking Data Scientists to join their team. An ideal candidate would be someone who is interested in developing and implementing novel approaches in translational medicine, or applying analytic and interpretive methods to integrate a wide variety of health and genomic data and leverage it towards improving treatment and prevention.
- Build machine learning and deep learning pipelines to assess risk for a number of diseases using integrated big data from various data sources, including genetics, genomics, electronic medical records, social, behavioral, and environmental information, wearable data, and medical imaging data.
- Standardize and normalize data extracted from electronic medical records using Common Data Models, such as OMOP, RxNorm, LOINC, and CCS.
- Identify novel indications or side effects for drugs prescribed to patients in certain settings, in order to recommend strategies to improve standard of care.
- Work with the Business Development team, which collaborates with pharmaceutical and insurance partners, to use aggregated patient data to assess a variety of clinical questions.
- Extensive experience in machine learning with a proven track record of developing and applying advanced computational techniques to solve complex disease problems
- Knowledge of advanced machine learning methodologies such as deep learning, computer vision on radiology or pathology images, and natural language processing
- Experience working with high-performance computing clusters, especially ones designed for AI/machine learning applications, such as AWS
- Familiarity with Common Data Models, such as OMOP CDM from OHDSI, is an advantage
- Highly proficient in programming and scripting in at least one language (R, Python, Julia, etc.)
- Experience with SQL and Oracle databases