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About This Role
This is a 100% remote position \- requires 1 day onsite a year at 150 E 42nd Street, New York, NY location
The Data Science Analyst II serves as a senior member of the Pharmacy Analytics team, advancing Mount Sinai’s mission through high\-impact data analysis, reporting, and automation to improve pharmacy financial performance, operational efficiency, and clinical decision\-making. This role works cross\-functionally with stakeholders from Pharmacy, Finance, 340B, Supply Chain, and Clinical Operations to deliver data products that inform strategic decisions across the Health System
The Analyst plays a key role in developing interactive dashboards and optimizing SQL queries to integrate data from Epic Clarity, Caboodle, Oracle Cloud, Azure, wholesaler systems, and other vendor sources. A strong understanding of pharmacy workflows, healthcare finance, and medication data is essential, as this position supports critical initiatives such as drug spend tracking, waste reduction, budget forecasting, inventory reconciliation, and 340B compliance reporting.
The Data Science Analyst II collaborates with stakeholders from across the organization to develop sophisticated analytics to provide information, insights and BI (Business Intelligence) solutions that contribute to sound strategic planning, decision\-making, goal setting, and effective performance measurement. The Data Science Analyst II demonstrates sound and a more advanced understanding of the healthcare domain, technical data manipulation and analytic development skills and impact the patient community of the Mount Sinai Health System.
- Analyzes data requests using information technology, enrollment, claims, pharmacy, clinical, contract, medical management, financial, administrative and other corporate data from both modeled and disparate internal and external sources.
+ Works with departmental staff to identify requirements for reporting and / or business intelligence tools.
+ Identifies necessary data, data sources and methodologies.
+ Collects, organizes, integrates, analyzes and interprets data.
+ Leverages advanced statistical analysis methods to create insightful recommendations and conclusions that may be communicated to the stakeholder.
+ Identifies and addresses expected and unforeseen data complexities to mitigate their impact on the analytic outcome and associated business decisions. Works to improve data quality where possible within created analytical models. Feeds data quality issues back to IT or identified data stewards to facilitate creation of high quality metrics.
+ Develops and may present reports, analyses and findings to senior management and others as scheduled or requested.
- Responsible for one or more of the following stakeholder groups:
+ Contracting and Commercialization May assist in the modeling and forecasting contract scenarios, measuring ongoing performance and identify trends in performance to inform our clinical or contracting staff to improve contract outcomes.
+ Care Management Helps to identify, understand and prioritize at\-risk members in need of care management. Helps stratify our membership to optimally use resources to focus on the patients most in need, currently or in the future.
+ Medical Directors Helps to identify utilization trends and variations across the different categories of health care services to assist the Medical Directors to focus their efforts to maximize contract performance and clinical effectiveness.
+ Quality and Documentation Helps to link payer quality and documentation opportunities into operational analytic processes to maximize our quality scores, top line revenue and optimize the use of resources in concert with MS Health System contracts.
+ I.T. / High Performance Computing in any ongoing projects.
- Takes a proactive role as liaison/analyst for internal stakeholders, understands their needs and translates them into reporting and analytic solutions.
- Effectively communicates with stakeholders and customers and ensures all requests are properly triaged, recorded and tracked.
- Adheres to corporate standards for performance metrics, data collection, data integrity, query design, and reporting format to ensure high quality, meaningful analytic output.
- Helps identify and understand data from internal and external sources for competitive, scenario and performance analyses, and financial modeling to gain member/provider insight into new and existing processes and business opportunities.
- Works closely with IT on the ongoing improvement of Mount Sinais integrated data warehouse, driven by strategic and business needs, and designed to ensure data and reporting consistency throughout the organization.
- Develops and maintains project work plans, including critical tasks, milestones, timelines, interdependencies and contingencies. Tracks and reports progress. Keeps stakeholders apprised of project status and implications for completion.
- Provides technical support to data analytics functions as they relate to varied business units, and technical expertise on the selection, development and implementation of various reporting and BI tools tied to business unit reporting requirements. Creates new BI reports and interactive dashboards as required.
- Prepares clear, well\-organized project\-specific documentation, including, at a minimum, analytic methods used, key decision points and caveats, with sufficient detail to support comprehension and replication.
- Ensures customers are adequately trained to use self\-service BI tools and dashboards.
- Mentors level I Analysts, and teaches others within the organization on how to a) define meaningful process and performance measures, b) develop BI queries, and c) generate and use management reports effectively.
- Shares development and process knowledge with other analysts in order to assure redundancy and continuously builds a core of analytical strength within the organization.
- Demonstrates advanced level proficiency with the principles and methodologies of process improvement. Applies these in the execution of responsibilities in support of a process focused approach.
- Other duties as assigned.
- BA or BS degree minimum, in a relevant field of study; Master's degree preferred.
- 5 years minimum in analytics development expertise, preferably in health care, or for a health provider, health plan or accountable care organization, including either:
+ Working knowledge of a health care EMR such as Epic/Clarity, aCW, etc.; a payor claims system such as Facets, Amisys, etc.; or a hospital/provider system such as IDX, Soarian, etc.
+ Advanced proficiency in SQL (Oracle/OCI preferred), Tableau, Excel, and working with large relational databases (Epic Clarity/Caboodle, Oracle Cloud).
+ Strong understanding of 340B program compliance, hospital pharmacy operations, and medication utilization.
+ Excellent communication skills with the ability to explain data insights to both technical and non\-technical audiences
+ PhD, MD or DO program may be substituted for three years of experience.
Non\-Bargaining Unit, 223 \- DTP Enterprise Data \& Analytics \- MSH, Mount Sinai Hospital
Strength through Unity and Inclusion
The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai’s unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture that ensures fair access to opportunities, promotes inclusive practices, and supports the success of every individual.
At Mount Sinai, our leaders are committed to fostering a workplace where all employees feel valued, respected, and empowered to grow. We strive to create an environment where collaboration, fairness, and continuous learning drive positive change, improving the well\-being of our staff, patients, and organization. Our leaders are expected to challenge outdated practices, promote a culture of respect, and work toward meaningful improvements that enhance patient care and workplace experiences. We are dedicated to building a supportive and welcoming environment where everyone has the opportunity to thrive and advance professionally. Explore this opportunity and be part of the next chapter in our history.
About the Mount Sinai Health System:
Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 48,000 employees working across eight hospitals, more than 400 outpatient practices, more than 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time — discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high\-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients’ medical and emotional needs at the center of all treatment. The Health System includes more than 9,000 primary and specialty care physicians; 13 joint\-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News \& World Report's Best Hospitals, receiving high "Honor Roll" status, and are highly ranked: No. 1 in Geriatrics, top 5 in Cardiology/Heart Surgery, and top 20 in Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News \& World Report’s “Best Children’s Hospitals” ranks Mount Sinai Kravis Children's Hospital among the country’s best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is ranked No. 11 nationwide in National Institutes of Health funding and in the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. Newsweek’s “The World’s Best Smart Hospitals” ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally.
Equal Opportunity Employer
The Mount Sinai Health System is an equal opportunity employer, complying with all applicable federal civil rights laws. We do not discriminate, exclude, or treat individuals differently based on race, color, national origin, age, religion, disability, sex, sexual orientation, gender, veteran status, or any other characteristic protected by law. We are deeply committed to fostering an environment where all faculty, staff, students, trainees, patients, visitors, and the communities we serve feel respected and supported. Our goal is to create a healthcare and learning institution that actively works to remove barriers, address challenges, and promote fairness in all aspects of our organization.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Mount Sinai Health System, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Mount Sinai Health System AI Hiring
Mount Sinai Health System has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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