Sr Manager Therapeutic Area Genetics
2 days ago
Regeneron Genetics Center is a wholly owned subsidiary of Regeneron Pharmaceuticals Inc. which is mission driven towards harnessing the power of human genetics to discover important new drug targets, validate existing research programs and optimize clinical trials. This TAG-CI (Therapeutic Area Genetics- Clinical Informatics) position is part of the Translational Genetics group where the goal is to tap into the growing database of more than 2.5 million sequenced exomes and associated deidentified health information to make meaningful biological discoveries at speed and scale. This role calls for expertise in data management, data science skills and use of biomedical informatics principles to create phenotypic analytical assets. Comfort in programming languages and technologies (python, R, SQL, web technologies), use of API’s for validation and light weight tool development to serve these purposes is expected. Working with internal IT and analytical teams to scale up solutions for supporting research will be key. The ideal candidate would be a PhD Scientist with a strong programming, research and epidemiology skills with formal training in a biomedical informatics program.
A typical day may include:
- Conduct data analysis, including mining and curating of phenotypic datasets with primary responsibility in developing and identifying complex clinical phenotypes and cohorts of interest for efficient data mining and association analysis in both phenotype first and genotype first queries.
- Understand analytical needs of a variety of therapeutic area researchers and translate them through queries on clinical databases, EHR datasets.
- Support and develop phenotyping approaches through collaboration with Genetic and IT teams. This will include strategizing underlying database schema, defining pipelines for data streams and help define dashboards and tooling for creating phenotype assets.
- Relevant to the role, the team's responsibilities encompass a broad range of tasks, including the acquisition, standardization, processing, and management of clinical data from diverse sources such as electronic health records, surveys, and health registries.
- Use RGC data resources for building out visualization and analytical tools. Data resources include data in common data model, DNAnexus and AWS S3 storage and a variety of other assets accessible through API’s.
- Demonstrate out of the box thinking to improve efficiencies and overall workflow pertaining to use of clinical phenotypes for GWAS ExWAS analyses.
- Participate in an agile delivery process to own, research, and recommend new solutions.
This role may be for you if:
- You have strong analytical skills.
- You have an eye for detail, thinking of scalability and overall emphasis operational excellence matters more than just finishing the tasks.
- You can multitask and manage simultaneous projects to meet deadlines with a strong attention to detail.
- You possess the ability to interpret and communicate analytical information in a clear, concise manner.
- You excel at managing relationships and projects involving diverse stakeholders. Ensuring that analyses are executed in a timely, accurate and reliable manner.
- You communicate findings clearly and document work for training and replication purposes.
To be considered for this role you must have a PhD or Clinical degree in a related field as well as at least 3 years of experience in biopharma, healthcare, life sciences, or a related field. At least 3 years of experience in applied health informatics (research or clinical operations) with solid understanding of API’s, web application architecture, python, React, NodeJS, Javascript. Excellent coding and quantitative skills and overall familiarity with modern data technologies and cloud compute environments. Experience with front end UI or mobile development is a plus. A solid foundation in clinical informatics. An NLM-sponsored fellowship or an accredited master's degree or certification in informatics is preferred. Experience with OHDSI tools and the OMOP data models. Deep knowledge of clinical data standards such as ICD, RxNorm, SNOMED, and LOINC and other biomedical ontologies. Appreciation of nuances of clinical data from different sources (coding, structure, classification) and how they can be used for deeper phenotyping as well as summative reporting. Excellent understanding of epidemiology and different study designs. Basic understanding of human genetic discovery approaches. Must be onsite 3-days a week subject to management discretion.
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