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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 a Predictive Analytics Analyst.
In this role, the successful candidate is responsible for leveraging advanced statistical and machine learning techniques to generate actionable insights from HR and business data. This role designs and develops predictive models (e.g., for performance, turnover, and hiring outcomes), automates data integration between spreadsheets and HR systems, and builds robust data pipelines to ensure timely, accurate reporting. Partnering closely with HR and cross-functional teams, the analyst translates complex analyses into clear, practical recommendations that support workforce planning, talent strategies, and organizational performance. The position requires strong technical skills in Python/R and relational databases, a solid grounding in statistics and data science, and the ability to communicate insights effectively to non-technical stakeholders while driving continuous improvement in People Analytics methodologies and practices.

Job Responsibilities:

• Lead or contribute to cross-functional People Analytics projects, overseeing data pipelines, predictive modeling, and stakeholder engagement.
• Perform advanced statistical analyses (e.g., tests, non-parametric tests, multivariate analysis, winsorization) to evaluate salary band positioning, tenure, performance trends, turnover analysis, and pay equity.
• Augment analytical report outputs with fact-based examples, observations, and recommendations that guide informed decision-making.
• Keep up to date with new academic and industry developments in data analytics, machine learning, data science, people analytics, and HR technology, and incorporate relevant methods into practice to continuously improve analytical methods and predictive modeling approaches.
• Provide data insights and suggestions to solve complex HR and business problems by formulating, developing, and interpreting statistical and machine learning models.
• Derive actionable insights from structured and unstructured HR data (e.g., HRIS, ATS, performance, engagement, compensation, etc.).
• Communicate analytical results clearly and succinctly through presentations, dashboards, and written narratives tailored to non-technical stakeholders.
• Work closely with HR and business leaders to make value-added analytical services available to internal customers.
• Be creative, collaborative and a self-starter, proactively identifying opportunities to use analytics to improve HR and business outcomes.

Minimum Qualifications:
To qualify you must have a Bachelor's Degree is required. Strong preference in Statistics, Mathematics, Computer Science, Data Science, Economics, industrial/systems engineering, information management, epidemiology, or other related quantitative skills. 3 or more years of experience in predictive modeling and data mining using large and complex datasets; experience in People Analytics / HR analytics strongly preferred (experience in other analytics-heavy domains such as insurance, financial services, or retail is also valuable). Hands-on experience with Python, R, SQL, cloud platforms, and machine learning. Hands-on experience building predictive models (e.g., linear and logistic regression, GLM, decision trees, cluster analysis, principal components, feature creation and validation) using Python and/or R. Experience leading or contributing to cross-functional analytics projects, coordinating data pipelines, modeling workstreams, and stakeholder communication. Data visualization and dashboarding (e.g., Power BI, Tableau, or similar). Strong written and verbal communication skills for explaining complex analyses to non-technical stakeholders.

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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