NYU Langone Health is a fully integrated health system that consistently achieves the best patient outcomes through a rigorous focus on quality that has resulted in some of the lowest mortality rates in the nation. Vizient Inc. has ranked NYU Langone the No. 1 comprehensive academic medical center in the country for three years in a row, and U.S. News & World Report recently placed nine of its clinical specialties among the top five in the nation. NYU Langone offers a comprehensive range of medical services with one high standard of care across 6 inpatient locations, its Perlmutter Cancer Center, and over 320 outpatient locations in the New York area and Florida. With $14.2 billion in revenue this year, the system also includes two tuition-free medical schools, in Manhattan and on Long Island, and a vast research enterprise with over $1 billion in active awards from the National Institutes of Health.

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Position Summary:
We have an exciting opportunity to join our team as an Analyst, Data Science – NYU Langone Heart.

In this role, the successful candidate NYU Langone Heart is a formal integration of Cardiology and CV surgery for the NYU Langone Health System. The mission of NYU Langone Heart is to align best practices to promote the highest standards in quality and efficiency. Furthermore, NYU Langone Hearts mission extends to expanding research, promoting innovation and growing the NYU Langone Health network. Position will support and develop the NYU Langone Heart Data Hub. Person will work with the existing NYU Langone Health Data Hub team for other service lines and divisions. The data hub team and engineer will provide support to make better health care decisions using information from available NYU Langone Health data. The task includes gathering, preparing, and integrating data from multiple sources, running advanced machine learning and statistical analyses, and communicating findings in a clear and objective way. You will also be responsible for designing, developing, and managing the infrastructure and tools necessary for acquiring, storing, processing, and analyzing large volumes of cardiovascular data. In addition to your core data engineering responsibilities, you will also have specific AI responsibilities related to cardiology, where you will collaborate with data scientists and researchers to develop and deploy AI models and algorithms in the field of cardiology.

Job Responsibilities:

Data Acquisition and Integration Identify, collect, and integrate diverse data sources related to cardiology, including electronic health records (EHR), imaging data, clinical trials data, wearables data, and other relevant sources. Implement efficient data pipelines and ETL processes to ingest and transform raw data into structured formats suitable for analysis. Data Storage and Management: Design and maintain scalable, reliable, and secure data storage systems, such as data warehouses, databases, and data lakes, to accommodate the growing volume and complexity of cardiology data. Optimize data storage and retrieval processes for improved performance and efficiency. Data Processing and Analysis: Develop and implement data processing workflows to cleanse, preprocess, and validate cardiology data, ensuring its accuracy, completeness, and consistency. Collaborate with data scientists and researchers to develop analytical models and algorithms for deriving insights from cardiology data, leveraging techniques such as machine learning, deep learning, and natural language processing. AI Model Development and Deployment: Work closely with data scientists and researchers to build, train, evaluate, and deploy AI models and algorithms specific to cardiology. Implement best practices for model versioning, monitoring, and retraining to ensure ongoing model performance and accuracy. Infrastructure and Tool Development: Evaluate, select, and implement appropriate tools, frameworks, and technologies for data engineering and AI development in the cardiology domain. Develop and maintain robust, scalable, and efficient infrastructure to support data engineering and AI operations. Collaboration and Communication: Collaborate with cross-functional teams, including data scientists, clinicians, researchers, and IT professionals, to understand their requirements and provide data engineering and AI expertise in the cardiology domain. Communicate effectively with stakeholders, presenting technical concepts and findings in a clear and understandable manner.

Additional Position Specific Responsibilities:

Understanding the health system and research needs so as to formulate, solve and restrict the slice of data to be explored.Collecting, and integrating data from various sources. Performing data cleaning, processing, and validation, in order to ensure its quality. Exploring and visualizing data. Utilizing deep learning, advanced machine learning, and statistical analysis to derive healthcare system and research insights, and to support predictive analytics. Clearly communicating the findings from the analysis to turn information into something actionable through reports, dashboards, and/or presentations. Supporting and Improving access to self-serve data analytics. Automating and Improving data pipelines. Implementing or consulting on projects and research requiring machine learning.

Minimum Qualifications:

Masters degree in a quantitative discipline (Biomedical Informatics, Computer Science, Machine Learning, Applied Statistics, Mathematics or similar field) At least 3 years of work 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 / MapReduce, Spark etc. Knowledge of databases (Relational / SQL, NOSQL, MongoDB, etc.) Good grasp of software engineering principles. 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 Experience working with EMR (Epic strongly preferred).

Preferred Qualifications:
1. Strong experience in data engineering, data management, and data analysis, preferably in the healthcare or cardiology domain.
2. Experience with data integration from multiple sources (Syngo, data lake, Cupid, and EPIC).
3. Proficiency in programming languages such as Python, SQL, and R.
4. Experience with data processing frameworks and tools, such as Apache Spark, Hadoop, or similar.
5. Familiarity with AI techniques and frameworks, including machine learning, deep learning, and natural language processing.
6. Knowledge of cardiology terminology, data formats, and standards, such as DICOM, HL7, and FHIR, is highly desirable.
7. Experience with cloud platforms and technologies, such as AWS, Azure, or Google Cloud, is a plus.
8. Extensive experience developing machine learning models in at least one deep learning framework like TensorFlow, or PyTorch.
9. Significant experience with project ownership and ML model lifecycle development: conception, data collection, training, evaluation, deployment, and monitoring.
10. Experience with popular statistical and machine learning techniques, such as clustering, linear regression, KNN, decision trees, etc.
11. Significant experience with project ownership and ML model lifecycle development: conception, data collection, training, evaluation, deployment, and monitoring.
12. Experience with popular statistical and machine learning techniques, such as clustering, linear regression, KNN, decision trees, etc.
13. Experience with distributed training for machine learning models.
14. Excellent communication skills and ability to effectively communicate with various stakeholders.
15. Strong background in machine learning theory and the capacity to understand, evaluate and implement cutting-edge machine learning research.
16. Excellent problem-solving skills, attention to detail, and ability to work in a fast-paced, collaborative environment.

Qualified candidates must be able to effectively communicate with all levels of the organization.

NYU Langone Health 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.

At NYU Langone Health, we are committed to supporting our workforce and their loved ones with a comprehensive benefits and wellness package. Our offerings provide a robust support system for any stage of life, whether it’s developing your career, starting a family, or saving for retirement. The support employees receive goes beyond a standard benefit offering, where employees have access to financial security benefits, a generous time-off program and employee resources groups for peer support. Additionally, all employees have access to our holistic employee wellness program, which focuses on seven key areas of well-being: physical, mental, nutritional, sleep, social, financial, and preventive care. The benefits and wellness package is designed to allow you to focus on what truly matters. Join us and experience the extensive resources and services designed to enhance your overall quality of life for you and your family.

NYU Langone Health 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.

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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 $120,483-133,870 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.

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