Data Scientist

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Now more than ever, our guiding principles are helping us in our search for exceptional talent - candidates who align with our unique workplace culture and who want to maximize the abundant opportunities for growth and success.
 
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HSS is consistently among the top-ranked hospitals for orthopedics and rheumatology by U.S. News & World Report. As a recipient of the Magnet Award for Nursing Excellence, HSS was the first hospital in New York City to receive the distinguished designation. Whether you are early in your career or an expert in your field, you will find HSS an innovative, supportive and inclusive environment.

Working with colleagues who love what they do and are deeply committed to our Mission, you too can be part of our transformation across the enterprise.

Emp Status

Regular Full time

Work Shift

Day (United States of America)

Compensation Range

The base pay scale for this position is $101,500.00 - $172,500.00. In addition, this position will be eligible for additional benefits consistent with the role. The salary of the finalist selected for this role will be determined based on various factors, including but not limited to: scope of role, level of experience, education, accomplishments, internal equity, budget, and subject to Fair Market Value evaluation. The hiring range listed is a good faith determination of potential compensation at the time of this job advertisement and may be modified in the future.What you will be doing

 

Data Scientist I / Data Scientist II (Remote or Hybrid NYC) 

Hospital for Special Surgery 

HSS Data, Data Science & Advanced Analytics 

 

Employment Status: Regular Full-Time 
Hours per Week (Estimated): 37.5 

Reports to: Senior Director of Data Science Hospital for Special Surgery (HSS) is the world’s leading academic medical center focused on musculoskeletal health, ranked #1 in orthopedics by U.S. News & World Report and Newsweek. Now, we’re building what comes next. 

 

HSS Data supports the enterprise side of HSS through four broad domains: Business, Clinical, Finance, and Research. The work is broader than machine learning. We build product features that forecast and predict various operational and clinical endpoints, test interventions, estimate program impact, find meaningful patient and operational segments, detect risk and anomalies, and build measurement frameworks when the question is not yet analytically well formed. We work in small “garages” with the people who own the problem, prototype quickly, and turn useful work into products and features that are deployed, monitored, and improved. The goal is velocity without sloppiness: get close to the work, build something real, measure whether it helped, and keep going. 

 

Job Summary 

 

We’re hiring Data Scientists at two levels to build advanced analytics and data-science-driven product features across HSS. Assignments will be fluid across the broad Business, Clinical, Finance, and Research domains based on organizational needs, the work underway, and a candidate’s strengths and interests.  

 

Data Scientist I. An early-career builder, generally with zero to two years of experience, who will usually begin with a narrower set of domains and problems. You’ll work with more experienced data scientists and domain experts to frame messy questions, build and test solutions, and personally learn to deploy, monitor, and improve models and features within applications and Databricks. Strong internship, graduate, or substantial applied-project experience can count. 

 

Data Scientist II. A more independent individual contributor, generally with three to five or more years of experience, who can own ambiguous work end to end and move across more than one domain when needed. You’ll find and shape opportunities, choose the right analytical approach, personally productionize and monitor the work, connect reusable methods across teams, and help earlier-career data scientists improve their game. 

 

What you’ll do 

 

• Find and frame the right problems. Work with domain experts to identify opportunities, including needs that were never articulated as “data-science requests”. Turn vague questions — “what will demand look like next quarter?”, “why are cases running late?”, or “did this intervention actually help?” — into well-posed problems with explicit assumptions, a decision or workflow they will change, and a credible way to measure impact and value.  

• Build data-science-driven products, not artifacts. Deliver advanced analytics and data-science-driven features through applications and real products (not stand-alone notebooks) that clinicians, operators, researchers, finance teams, and executives can rely on and keep using. Own or contribute across design, prototyping, development, testing, deployment, integration, monitoring, and iteration. 

• Use the right analytical method for the decision. Apply predictive modeling and forecasting, experimentation and A/B testing, causal or program-effectiveness analysis, segmentation, anomaly detection, risk stratification, and novel measurement where each is appropriate. Challenge weak designs, quantify uncertainty, and know when a simpler method is the better answer. 

• Personally productionize the work. Write maintainable code, integrate models and analytical logic into applications and Databricks workflows, create appropriate evaluation and monitoring, respond to drift or failure, and keep improving the product after launch. We partner with other groups within HSS Data, but do not throw a notebook over the wall and call the work finished. 

• Build reusable capability. Improve shared approaches for rapid model development, feature and evaluation pipelines, model cards, monitoring, documentation, and reproducibility. Recognize when a method, dataset, feature, or workflow built in one domain can solve a problem elsewhere. 

• Work in “garages”. Build collaboratively with domain experts at the hip during daily, synchronous working sessions. Push back, ask questions, change course on new evidence, and explain complex quantitative ideas clearly to clinical, operational, research, finance, and executive audiences. 

• Work with AI fluently, but skeptically. Use agentic coding tooling to move fast, with the judgment to know what to delegate and what to think hard on, and the instinct to catch quiet mistakes and implicit assumptions that should be challenged. 

 

Qualifications 

 

• Bachelor’s degree in a quantitative field (e.g., data science, statistics, computer science, engineering, applied math, economics, operations research, or similar), or equivalent demonstrated technical ability.  

• Master’s or PhD training is preferred but not required, particular for Data Scientist II roles. For all roles, we weigh demonstrated ability over credentials. 

• Strong data science, statistics, and modeling foundations. The ability to explain and defend modeling and design choices, understand the methods you invoke, spot leakage and confounding, quantify uncertainty, and insist on performance metrics tailored to the problem at hand.  

• Strong programming ability and experience (e.g., Python, SQL). Comfort working with git, building within applications, and working in a cloud/lakehouse environment; we use Databricks and AWS. 

• Prior Databricks experience is preferred. 

• Prior experience working on projects involving large datasets, including leading the design, prototyping, development, testing, deployment, and monitoring of statistical or machine learning models. Specific experience building data-science-driven features, progressing work from problem ideation and formulation to live app-based product features or decision-support tools that users really used, were monitored, and were iterated on. 

• Data Scientist I: Generally 0-2 years of professional data science or advanced analytics experience. Strong graduate, internship, fellowship, research, or substantial applied-project work can count. DS I candidates should have exposure to end-to-end production experience or must be ready to learn this quickly. 

• Data Scientist II: Generally 3-5+ years of professional data science or advanced analytics experience, with a record of independently leading work from problem discovery and formulation through deployment, application integration, monitoring, and iteration. DS II candidates must already have substantial end-to-end production experience. 

• Experience in one or more of forecasting, experimentation, causal inference, anomaly detection, risk stratification, optimization, or measurement design. 

• Experience with healthcare data, in a US-based healthcare delivery or health-tech setting, especially in musculoskeletal health and orthopedics, is preferred. Other useful domain backgrounds may include operations, finance, economics, research, clinical science, consumer or digital products, or other complex enterprise settings. 

• Strong experience with modern AI agentic coding tools and techniques. Robust skepticism and the judgment to discern what tasks are best left to AI and what tasks are fundamentally human (framing objectives and goals, verifying work aligns with those). The ability and curiosity to keep up on the latest models, their strengths and weaknesses, and effective strategies for using them. 

• High intelligence and ability to frame messy problems into structured ones, including exercising judgment about assumptions and unknowns, and what actually matters for a given problem (things that AI cannot do for us).  

• Flexibility, communication, collaboration, comfort with ambiguity, and the ability to leverage those skills to ramp up quickly in a new environment, work well with domain experts who are not data experts, translate complex concepts for those audiences without being steamrolled, and course correct with new information.  

• Drive and vision with a bias toward action and ownership. Having a point of view about where things should go, not just how to execute a direct ask or ticket. DS II candidates particularly should be able to operate autonomously, lead through influence rather than authority, juggle competing priorities, mentor others, and connect work across domains. 

 

Non-Discrimination Policy 

Hospital for Special Surgery is committed to providing high quality care and skilled, compassionate, reliable service to our community in a safe and healing environment. Consistent with this commitment, Hospital for Special Surgery provides care, admits, and treats patients and provides all services without regard to age, race, color, creed, ethnicity, religion, national origin, culture, language, physical or mental disability, socioeconomic status, veteran or military status, marital status, sex, sexual orientation, gender identity or expression, or any other basis prohibited by federal, state, or local law or by accreditation standards. 

Non-Discrimination Policy
Hospital for Special Surgery is committed to providing high quality care and skilled, compassionate, reliable service to our community in a safe and healing environment. Consistent with this commitment, Hospital for Special Surgery provides care, admits, and treats patients and provides all services without regard to age, race, color, creed, ethnicity, religion, national origin, culture, language, physical or mental disability, socioeconomic status, veteran or military status, marital status, sex, sexual orientation, gender identity or expression, or any other basis prohibited by federal, state, or local law or by accreditation standards.