The Icahn School of Medicine at Mount Sinai seeks a Data Scientist to advance the state-of-the-art in clinical informatics by developing novel algorithms for electronic health records analysis, focusing on advanced phenotyping and information retrieval, to address critical challenges in biomedical data science.
Requirements
- Experience with at least one programming language among Scala, Python, Java, C, or C++.
- Experience with database languages (e.g., SQL, NoSQL)
- Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP)
- Experience with version control systems (e.g., Git)
- Knowledge of big data technologies (e.g., Hadoop, Spark)
- Experience working with electronic health records data and clinical databases
- Familiarity with clinical phenotyping methodologies and natural language processing for clinical text
Responsibilities
- Design, develop, and implement advanced deep learning algorithms for electronic health records (EHR) analysis, with emphasis on patient phenotyping and information retrieval
- Apply graph neural networks to model complex relationships within clinical data and healthcare networks
- Utilize deep learning frameworks (TensorFlow, PyTorch) to build scalable and robust predictive models
- Integrate principles of statistical mechanics into algorithm development for enhanced modeling of complex physiological systems
- Collaborate with clinical researchers, physicians, and interdisciplinary teams to translate algorithmic innovations into clinically meaningful applications
- Conduct rigorous validation and evaluation of developed algorithms using appropriate statistical methods
- Contribute to scientific publications and present research findings at academic conferences
Other
- Master's degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Information Technology, Mathematics, Physics) or equivalent practical experience.
- 2 years of work experience in data science, software engineering, or data analysis
- Self-motivated with a demonstrated ability to work independently, and to exercise independent judgment in developing complex techniques or programs in a dynamic environment.
- Knowledge of healthcare data standards (FHIR, OMOP, ICD, SNOMED-CT)
- Experience with distributed computing and cloud platforms (AWS, Google Cloud, Azure)
