Demo

Associate Data Science Analyst / Engineer - Predictive Analytics (HYBRID NYC)

NYU Langone
New York, NY Full Time
POSTED ON 1/26/2025
AVAILABLE BEFORE 3/25/2025

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 NYU Langone Health, 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, diversity, 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 diversity, inclusion, and individual skills, ideas, and knowledge. For more information, go to med.nyu.edu, and interact with us on LinkedInGlassdoorIndeed, FacebookTwitter and Instagram.

Position Summary:
We have an exciting opportunity to join our team as a Associate Data Science Analyst / Engineer.

In this role, the successful candidate will work with the Division of Applied AI, which is a data science team that is a part of the Medical Center IT MCIT Department of Health Informatics and the Center for Healthcare Innovation and Delivery Science. The mission of the unit is to apply the latest advances in machine learning and predictive analytics toward solving clinically and operationally significant prediction and classification problems across the medical center. We are both an operational and academic group that is interested in applying machine learning in healthcare settings and pushing the boundaries of academic research. This individual will be positioned to contribute to analytics efforts at NYU By teaming up with clinicians, operations and administrative teams.  The individual will develop innovative and impactful data driven healthcare solutions. The Data Scientist/Engineer will report to Dr Vincent Major while working closely with other members of the unit and NYU healthcare community at large.

Job Responsibilities:

  • Prepare and present results, reports, both oral and written, to a variety of audiences, concerning processes, models, evaluation, and impact (ROI)
  • 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
  • Collaborate with the overall NYULMC healthcare community and contribute to ongoing predictive modeling / analytics efforts

Minimum Qualifications:
To qualify you must have a 1 Masters degree in a quantitative discipline Biomedical Informatics Computer Science Machine Learning Applied Statistics Mathematics or similar field 2 Proficiency in at least one programming language Python R and machine learning tools scikit learn R 3 Knowledge of predictive modeling and machine learning concepts including design development evaluation deployment and scaling to large datasets4 Familiarity with computing models for big data Hadoop / MapReduce Spark etc 5 Knowledge of databases Relational / SQL NOSQL MongoDB etc 6 Good grasp of software engineering principles Experience in integrating modern software architectures7 Knowledge and some experience in operational aspects of software development and deployment including automation testing virtualization and container technology8 Knowledge of clinical and operational aspects of healthcare delivery9 Excellent written and oral communication skills for a variety of audiences

Preferred Qualifications:
1 PhD degree in a quantitative field Biomedical Informatics Computer Science Machine Learning Applied Statistics Mathematics or similar field 2 years experience2 Demonstrated skills in design and implementation of complex machine learning models3 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 provides its staff with far more than just a place to work. Rather, we are an institution you can be proud of, an institution where you'll feel good about devoting your time and your talents.

NYU Grossman School of Medicine is an equal opportunity and affirmative action employer committed to diversity and inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sex, sexual orientation, transgender status, gender dysphoria, national origin, age, religion, disability, military and veteran status, marital or parental status, citizenship status, genetic information or any other factor which cannot lawfully be used as a basis for an employment decision. We require applications to be completed online.
If you wish to view NYU Grossman School of Medicine's EEO policies, please click here. Please click here to view the Federal "EEO is the law" poster or visit https://www.dol.gov/ofccp/regs/compliance/posters/ofccpost.htm for more information.

NYU Langone Health provides a salary range to comply with the New York state Law on Salary Transparency in Job Advertisements. The salary range for the role is $67,771.14 - $110,240.00 Annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.

To view the Pay Transparency Notice, please click here

Salary : $1,000 - $1,000,000

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