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About This Role
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 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.
*For more information, go to* *med.nyu.edu*, and interact with us on *LinkedIn*, *Glassdoor*, *Indeed*, *Facebook*, *Twitter* *and* *Instagram*.
Position Summary:
We have an exciting opportunity to join our team as a Data Science Analyst / Engineer.
We are seeking a qualified Data Scientist Analyst to work within the Division of Applied AI Technologies at NYU Langone Health. The 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.
Job Responsibilities:
- Design and build data solutions using state\-of\-the\-art machine learning and informatics methods.
- Work with large, complex, and noisy clinical datasets to solve challenging problems in the healthcare domain.
- Build and engineer workflows to collect, store, clean, and process structured and unstructured data, and verify their integrity and appropriateness for specific business processes and analytics systems.
- Deploy and integrate these products within the larger ecosystem of healthcare infrastructure at NYULMC.
- Prepare and present oral and written results and reports to a variety of audiences regarding processes, models, evaluation, and impact (ROI).
- Lead high\-priority data\-oriented projects, including development, deployment, and evaluation.
- Collaborate with associate data scientists, engineers, and clinically trained professionals to test novel approaches and support the implementation, maintenance, and improvement of predictive models.
- Collaborate with the broader NYULMC healthcare community and contribute to ongoing predictive modeling and analytics efforts.
- Establish key metrics, run experiments, and perform analyses to understand the incremental impact of efforts to drive adoption and engagement.
- Contribute to data science best practices and mentor team members to elevate the technical capabilities and impact of the group.
- Quantify the impact of model improvements and communicate trade\-offs to stakeholders.
- Build evaluation frameworks, validate model behavior prospectively and silently, and oversee ongoing maintenance.
- Design systems, infrastructure, abstractions, and processes that accelerate development time for future projects.
- Manage and plan go\-live activities and define the necessary steps to ensure timely delivery.
- Balance perfection with production readiness, iterate quickly, and recognize when a minimum viable product outperforms a six\-month research project.
- Contribute ideas and innovations to solve internal and stakeholder problems.
Minimum Qualifications:
To qualify you must have a Masters degree in a quantitative discipline (Biomedical Informatics, Computer Science,
Machine Learning, Applied Stascs, 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
(scikitlearn, 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 soware engineering principles. Experience in integrating modern
software architectures.
Knowledge and some experience in operational aspects of soware 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
Preferred Qualifications:
PhD degree in a quantitative field (Biomedical Informatics, Computer Science, Machine
Learning, Applied Stascs, Mathematics or similar field) \+ 2 years experience.
Demonstrated skills in design and implementation of complex machine learning models.
Demonstrated knowledge of soware 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. 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 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.
View Know Your Rights: Workplace discrimination is illegal.
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 $84,577\.92 \- $110,250\.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 Context
This $84K-$110K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At NYU Langone Health, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($97K) sits 49% below the category median. Disclosed range: $84K to $110K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NYU Langone Health AI Hiring
NYU Langone Health has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, Research Engineer. Based in New York, NY, US. Compensation range: $90K - $140K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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