Data Science Analyst​/Engineeer

NYU Grossman School of Medicine is one of the nation’s top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care.

At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.

We have an exciting opportunity to join our team as a Data Science Analyst/Engineer. The Imaging Data Scientist’s primary roles are to process and analyze digital images of tissues using commercially available platforms (e.g., Halo, Visio Pharm, SciLS, etc.) and to visualize and perform computational and statistical analysis of the resulting large datasets using languages such as R, Python, C++, Java, and/or MATLAB.

The Imaging Data Scientist will prepare results for internal meetings and public dissemination in posters, talks, and papers.

• Prepare and present results, reports, both oral and written, to a variety of audiences, concerning processes, models, evaluation, and impact (ROI)
• Performs other duties as assigned.
• Lead specific data‑oriented projects of high importance, including development, deployment, and evaluation
• Design and build data solutions using state‑of‑the‑art machine learning and informatics methods; deploy and integrate these products within the larger ecosystem of healthcare infrastructure at NYULMC
• Work with large, complex, and noisy clinical datasets for solving challenging problems in the healthcare domain
• Build and engineer workflows to collect, store, clean and process structured as well as unstructured data, verify their integrity and appropriateness for specific business processes and analytics systems
• Manage associate data scientists, engineers, and clinically trained professionals for testing novel approaches, implementation, maintenance, and improvement of predictive models
• Collaborate with the overall NYULMC healthcare community and contribute to ongoing predictive modeling / analytics efforts

• Masters degree in a quantitative discipline (Biomedical Informatics, Computer Science, Machine Learning, Applied Statistics, Mathematics, or similar field) and 3+ years of experience in machine learning/data science
• Proficiency in at least one programming language (Python, R) and machine learning tools (scikit‑learn, R)
• Knowledge of predictive modeling and machine learning concepts, including design, development, evaluation, deployment, and scaling to large datasets
• Familiarity with computing models for big data (Hadoop/Map Reduce, Spark, etc.)
• Knowledge of databases (Relational/SQL, No
  
  SQL/Mongo
  
  DB, etc.)
• Good grasp of software engineering principles and experience in integrating modern software architectures
• Knowledge and some experience in operational aspects of software development and deployment, including automation, testing, virtualization, and container technology
• Knowledge of clinical and operational aspects of healthcare delivery
• Excellent written and oral communication skills for a variety of audiences

• PhD degree in a quantitative field (Biomedical Informatics, Computer Science, Machine Learning, Applied Statistics, Mathematics, or similar field) and 2+ years experience
• Demonstrated skills in design and implementation of complex machine learning models
• Demonstrated knowledge of software engineering and operational skills through prior projects
• Qualified candidates must be able to effectively communicate with all levels of the organization

NYU Grossman School of Medicine is an equal‑opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration. We require applications to be completed online.