Data Analyst I - Artificial Intelligence & Human Health
Description
The Data Analyst I is responsible for implementing best information technology data management practices in an effort to improve and ensure quality, streamline data cleaning efforts and deliver analyzable databases within agreed upon project timelines.
We are inviting applications for a Data Analyst position in data science and machine learning with a specific interest in applications to clinical data, including electronic health records (EHR).
Key topics of interest include but are not limited to: natural language processing, large language models, predictive modeling for disease outcomes and personalized therapeutic recommendations, working with common data model EHR standards (e.g., OMOP), multi-modal (e.g., genomics, imaging, and clinical data) deep learning for clinical decision support, unsupervised learning to discover biologically-relevant disease subtypes, among others. We have an interest in many disease domains including nephrology, cardiology, radiology, psychiatry, and others.
Data and Resources:
Data resources include (a) Access to >8 million patient records in the Mount Sinai Data Warehouse (b) The BioMe Biobank Program with >30,000 patients with whole exome sequencing data linked to longitudinal clinical data. The candidates will have the opportunity to develop their own research projects and to lead or participate in local as well as international collaborations.
The analyst will join a dynamic team of data scientists, geneticists, and clinicians and participate in unique opportunities to apply deep learning for important scientific breakthroughs and to directly impact patients’ lives in a clinical setting.
Responsibilities
• Designs and writes custom applications needed to ensure the database meets requirements for the entry, management and reporting of data.
• Assists in the development of data management plans and guidelines that will deliver accurate, timelines consistent with quality data. Assures the quality control compliance process.
• Identifies and implements solutions to data management issues and concerns that arise during the conduct of research studies.
• Assists staff with technical issues related to electronic and remote record keeping systems; trains staff to help improve quality of data collected.
• Assists medical students, interns, residents and/or program faculty with queries and generating statistical analyses.
• Maintains knowledge of the current regulations and technologies related to data management.
• May write and prepare manuscripts and other materials for internal and external audiences.
• Performs other related duties.
Qualifications
• Bachelors degree in computer science, statistics and/or related field, or combination of equivalent work experience and education. Masters degree in relevant field of study preferred.
• 2-3 years database application/management experience, preferably in a large medical center or healthcare environment. Experience in a quantitative science-related field (e.g., biomedical informatics, clinical informatics, machine learning, biostatistics, genetics, etc.) and Expertise with programming and statistical software experience in R and/or Python are preferred.
• Significant experience in biostatistics/machine learning techniques is preferred, ideally with published work and/or code available. Expertise with deep learning frameworks is preferred (e.g., TensorFlow, PyTorch, Keras). also preferred.
Apply Now
Apply Now
The Data Analyst I is responsible for implementing best information technology data management practices in an effort to improve and ensure quality, streamline data cleaning efforts and deliver analyzable databases within agreed upon project timelines.
We are inviting applications for a Data Analyst position in data science and machine learning with a specific interest in applications to clinical data, including electronic health records (EHR).
Key topics of interest include but are not limited to: natural language processing, large language models, predictive modeling for disease outcomes and personalized therapeutic recommendations, working with common data model EHR standards (e.g., OMOP), multi-modal (e.g., genomics, imaging, and clinical data) deep learning for clinical decision support, unsupervised learning to discover biologically-relevant disease subtypes, among others. We have an interest in many disease domains including nephrology, cardiology, radiology, psychiatry, and others.
Data and Resources:
Data resources include (a) Access to >8 million patient records in the Mount Sinai Data Warehouse (b) The BioMe Biobank Program with >30,000 patients with whole exome sequencing data linked to longitudinal clinical data. The candidates will have the opportunity to develop their own research projects and to lead or participate in local as well as international collaborations.
The analyst will join a dynamic team of data scientists, geneticists, and clinicians and participate in unique opportunities to apply deep learning for important scientific breakthroughs and to directly impact patients’ lives in a clinical setting.
Responsibilities
• Designs and writes custom applications needed to ensure the database meets requirements for the entry, management and reporting of data.
• Assists in the development of data management plans and guidelines that will deliver accurate, timelines consistent with quality data. Assures the quality control compliance process.
• Identifies and implements solutions to data management issues and concerns that arise during the conduct of research studies.
• Assists staff with technical issues related to electronic and remote record keeping systems; trains staff to help improve quality of data collected.
• Assists medical students, interns, residents and/or program faculty with queries and generating statistical analyses.
• Maintains knowledge of the current regulations and technologies related to data management.
• May write and prepare manuscripts and other materials for internal and external audiences.
• Performs other related duties.
Qualifications
• Bachelors degree in computer science, statistics and/or related field, or combination of equivalent work experience and education. Masters degree in relevant field of study preferred.
• 2-3 years database application/management experience, preferably in a large medical center or healthcare environment. Experience in a quantitative science-related field (e.g., biomedical informatics, clinical informatics, machine learning, biostatistics, genetics, etc.) and Expertise with programming and statistical software experience in R and/or Python are preferred.
• Significant experience in biostatistics/machine learning techniques is preferred, ideally with published work and/or code available. Expertise with deep learning frameworks is preferred (e.g., TensorFlow, PyTorch, Keras). also preferred.
Apply Now
Apply Now