DescriptionThe Flex Staff will work in Ensari Lab within the Department of AI and Human Health and primarily work on EHR-based and genomic data analyses using the Mt Sinai data warehouse (MSDW). Our group conducts research in the intersection of biomedical informatics, artificial intelligence, and women’s health. Currently, we are using patient-generated data (e.g., wearables, sensors, electronic health records) and large language models (LLMs), along with genomic variables, to investigate female reproductive disorders. The ideal candidate will possess a combination of technical, domain-specific, and soft skills to analyze complex datasets, evaluate data quality, and communicate insights clearly to technical and non-technical stakeholders. The candidate will primarily work on a pilot study that is funded by the Women’s Biomedical Health Research Institute at Mount Sinai.
Responsibilities- Extract patient-generated health data from MSDW and genotypic and phenotypic data from BioMe Bank, ensuring data integrity, privacy, and security.
- Communications with research coordinators and staff at MSDW regarding data integrity and acquisition (as needed)
- Collaborate with postdoctoral fellows and students in the lab in the training, fine-tuning, and evaluation of large language models
- Process, clean, and organize large datasets, making them suitable for analysis and modeling.
- Collaborate with postdoctoral fellows and students in data analysis and implementing new methods for analyzing genomic data and LLM-based research
- Work closely with healthcare professionals to understand the context and implications of data findings
- Conduct literature reviews on the project topic to inform the research methods and contextualize findings
Qualifications- Depending on the role, a minimum of a high school diploma or GED is required as per MS Health System hiring policy. Otherwise, Bachelors, Masters or Ph.D as applicable.
- Prior experience in a quantitative methodology-based research lab or research environment
- Basic understanding of statistical methods, machine learning concepts, with a willingness to learn and apply new techniques as needed
- Excellent communications skills, ability to work collaboratively in a multi-disciplinary environment, strong problem solving