Data Scientist -Clinical Informatics
Sema4 is a patient-centered health intelligence company founded on the idea that more information, deeper analysis, and increased engagement will improve the diagnosis, treatment, and prevention of disease. Sema4 is dedicated to transforming healthcare by building dynamic models of human health and defining optimal, individualized health trajectories, starting in the areas of reproductive health and oncology. Centrellis™, our innovative health intelligence platform, is enabling us to generate a more complete understanding of disease and wellness and to provide science-driven solutions to the most pressing medical needs. Sema4 believes that patients should be treated as partners, and that data should be shared for the benefit of all.
Our Clinical Informatics team is seeking a Data Scientist to join our team of highly motivated and passionate scientists and engineers working to improve health. We are looking for a candidate 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. Our goal is to use these data to improve diagnostics, identify novel treatments, and offer clinical insights into both disease and wellness. We aim to deliver clinical applications into practice that will help clinicians target treatment and care to individuals’ health profiles rather than relying on a one-size-fits-all model, in order to improve patient outcomes. We are developing innovative new tools that address a range of needs prioritized by our physician partners using a vast repository of health and genomic data. The right candidate will work with software engineers and other data scientists on the Clinical Informatics team, others in R&D, including the Bioinformatics team, as well as the Product and Business Development teams in a multidisciplinary environment.
- Develop and apply novel computational methods for disease subtype stratification and digital phenotyping. Collaborate with team members and other scientists to ensure digital phenotypes can be adopted by hospitals in the eMERGE Consortium across the US.
- 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.
- PhD in Computer Science, Computer Engineering, Statistics, Bioinformatics or Computational Biology related field.
- 2+ years post-graduate experience of analyzing large data sets in healthcare/biotech/pharma with advanced analytics approaches, or equivalent PhD research experience.
- 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
- Excellent written and oral communication skills and ability to build strong relationships
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