Data Scientist I - AI & Human Health

Data Scientist 1 will be responsible for developing and enhancing machine learning products. They will collaborate with a multidisciplinary team of engineers and clinicians to address a wide range of issues related to clinical effectiveness and process improvement. This position will apply scientific rigor and statistical methods to the challenges of product development and enhancement, while considering the behaviors of the end user.

The AIMS Lab at the Icahn School of Medicine at Mount Sinai seeks a highly skilled Data Scientist to join our team within the Windreich Department of Artificial Intelligence and Human Health. This position offers an exceptional opportunity to advance the state-of-the-art in clinical informatics by developing novel algorithms for electronic health records analysis, with a focus on advanced phenotyping and information retrieval.

The successful candidate will leverage expertise in deep learning, statistical mechanics, and graph neural networks to build innovative computational solutions that address critical challenges in biomedical data science. This role is ideal for a researcher passionate about translating cutting-edge deep learning techniques into impactful clinical applications.

• 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

• Maintain comprehensive documentation of methodologies, code, and experimental results

• Stay current with emerging trends in machine learning, clinical informatics, and computational healthcare

• 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
• 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)
• Self-motivated with a demonstrated ability to work independently, and to exercise independent judgment in developing complex techniques or programs in a dynamic environment.

Preferred Qualifications

• Experience working with electronic health records data and clinical databases

• Knowledge of healthcare data standards (FHIR, OMOP, ICD, SNOMED-CT)

• Familiarity with clinical phenotyping methodologies and natural language processing for clinical text

• Experience with distributed computing and cloud platforms (AWS, Google Cloud, Azure)

• Publications in peer-reviewed journals or conferences in machine learning, computational biology, or medical informatics

• Experience with version control systems (Git) and collaborative software development practices

 

 

Strength through Unity and Inclusion

The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai’s unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture