Flex Staff - Part Time Data Analyst - Windreich Department of AI & Human Health

The Flex Staff Hourly employee is a variable one, ranging from entry level clerical support to professional (non-licensed), engaged by a department on an as-needed basis, compensated on an hourly basis. Flex Staff employees also include those who retired or terminated from a full-time position, brought back to perform their role on a part-time, temporary basis, often to support or orient the replacement employee.

The 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) to investigate female reproductive disorders. The candidate will primarily work on a pilot study that is funded by a Transatlantic Pilot Seed Fund from the Hasso Plattner Institute between Mount Sinai and Potsdam (Germany). The ideal candidate will possess a combination of technical, engineering, and soft skills to help launch a pilot study that is developing an AI-based patient self-management tool through lifestyle behaviors (i.e., physical activity) for pelvic pain management. 

 

• Help Extract patient-generated health data from MSDW and genotypic and phenotypic data from BioMe Bank, ensuring data integrity, privacy, and security.
• Assist the data analyst process, clean, and organize datasets for analysis.
• Collaborate with other lab personnel to train and evaluate large language models 
• Collaborate with researchers and engineers to design, build, and maintain the backend systems of an AI-based patient self-management App, ensuring robust, scalable, and secure integration of machine learning components for digital health interventions
• Oversee and manage the collection, validation, and secure storage of incoming study data, implementing best practices for data integrity, quality assurance, and adherence to research protocols
• Facilitate effective communication with study participants and cross-institutional teams, coordinating participant engagement, onboarding, and app-related support to maximize user experience and study retention

• 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.
• Experience relevant to the position.
• Knowledge of cloud platforms (such as AWS, Azure, or GCP) and containerization/orchestration tools like Docker and Kubernetes for deploying robust, scalable backend systems
• Understanding of security and privacy best practices, especially related to healthcare data (encryption, HIPAA/FHIR compliance, secure authentication, and access controls)
• Interests: A strong interest in women’s health, LLM and healthcare applications.
• Experience: Prior experience in a quantitative methods OR software engineering in a research environment
• Soft Skills: Excellent communication skills, ability to work collaboratively in a multidisciplinary environment, strong problem-solving abilities.

 

 

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