Data Scientist I AI & Human Health

Description

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

• Masters 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