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Senior Data Scientist
Position Summary
Works closely with multi\-disciplinary teams, including intuitional leaders and other key stakeholders in the development and implementation of data driven insights and strategies to advance institutional goals and mission. Performs advanced analyses of structured and unstructured data to solve multiple and/or complex problems using advanced statistical techniques, mathematical analyses (including machines learning), and knowledge of the organization and/or industry.
Essential Job Duties
- Assists in translating project objectives into discrete and comprehensive description of necessary data and other resources. Aids in the development plan(s) to mitigate impact due to any limitations in available data/resources.
- Assists in the definition of milestones, deliverables, and estimates timelines for all assigned projects. Assumes an ownership role of some tasks, as appropriate, and effectively coordinates across teams to reach defined milestones.
- Identifies and accesses necessary data assets across cloud and on\-premises data warehouses.
- Collaborates with subject matter experts to ensure accurate data definitions and to validate quality of all data endpoints as required.
- Utilizes strong programming skills to evaluate, explore, model, and integrate data from a variety of sources (EHR, national databases, other registries, other data systems/platforms). Demonstrates experience working with data from diverse modalities (structured, unstructured).
- Develops and/or uses algorithms and statistical predictive models. Applies proficient knowledge in algorithms and predictive models to investigate problems, detect patterns, and relate findings to project’s objective(s).
- Demonstrates proficiency in the end\-to\-end data science pipeline from data ingestion and cleaning to experimenting with predictive models to deployment of results.
- Writes, validates, and executes code to perform required modeling or analytical tasks by way of cloud and local compute environments (CPU or GPU).
- Creates informative and detailed reports, as well as data visualizations to summarize data and results; understands and evaluates appropriate performance metrics.
- Actively engages in meetings with stakeholders. Contributes content to, and may lead, presentations or auxiliary discussions of summary reports with audience.
- Follows best practices in documentation and use of code repositories, containers/environments, and version control. Works to ensure best practices are upheld and enforced.
- Adheres to all institutional policies and practices for appropriate use and safeguarding of data.
- Assists junior data scientists with tasks, as appropriate.
- Maintains multiple projects simultaneously and functions effectively both independently and as part of a team.
- Keeps abreast of innovative technologies and approaches pertinent to data science; evaluates their applicability and fit for current projects.
- Performs other special projects and duties as assigned.
Required Qualifications
- Graduate Degree in Medicine, Biomedical Informatics, Epidemiology, Computer Science, Data Science, Engineering, Public Health, Economics, Biostatistics, Health Policy, Statistics, Biometrics, or equivalent experience
- 3\+ years of research experience in managing and analyzing data, and proven expertise working with clinical and research information systems
- Strong analytic skills and ability to use a variety of software tools such as R, SAS, SQL, MS Excel, and MS Access
- Experience analyzing and interpreting data, maintaining and managing large datasets, and ensuring the integrity of the data
- Outstanding interpersonal skills and the ability to excel in team collaboration as well as in independent work
Preferred Qualifications
- Doctorate Degree in Medicine, Biomedical Informatics, Epidemiology, Computer Science, Data Science, Engineering, Public Health, Economics, Biostatistics, Health Policy, Statistics, or Biometrics
Join a healthcare system where employee engagement is at an all\-time high. Here we foster a culture of respect, belonging, and inclusion. Enjoy comprehensive and competitive benefits that support you and your family in every aspect of life. Start your life\-changing journey today.
Please note that all roles require on\-site presence (variable by role). Therefore, all employees should live within a commutable distance to NYP.
NYP will not reimburse for travel expenses.
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*NewYork\-Presbyterian Hospital is an equal opportunity employer.*
Salary Range:
$97,000\-$145,000/Annual
*It all begins with you. Our amazing compensation packages start with competitive base pay and include recognition for your experience, education, and licensure. Then we add our amazing benefits, countless* *opportunities for personal and professional growth and a dynamic environment that embraces every person. Join our team and discover where amazing works.*
Salary Context
This $97K-$145K 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 NewYork-Presbyterian Hospital, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($121K) sits 37% below the category median. Disclosed range: $97K to $145K.
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.
NewYork-Presbyterian Hospital AI Hiring
NewYork-Presbyterian Hospital has 2 open AI roles right now. They're hiring across Data Scientist. Based in Manhattan, NY, US. Compensation range: $111K - $145K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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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