Bachelor's degree in a quantitative field or equivalent technical ability; Master's preferred for Data Scientist II roles.
Strong foundation in data science, statistics, and modeling with the ability to explain and defend choices.
Proficient in programming (Python, SQL) and comfortable in cloud environments like Databricks and AWS; prior Databricks experience preferred.
Experience with large datasets and end-to-end production of statistical and machine learning models; ideal candidates have built data-driven features.
Experience in forecasting, experimentation, causal inference, or anomaly detection; healthcare data experience preferred, especially in musculoskeletal health.
Familiarity with modern AI coding tools and techniques, with a critical approach to their application.
Strong problem-framing skills and the ability to navigate ambiguity while collaborating with non-expert stakeholders.
Responsibilities
Identify and frame key problems by collaborating with domain experts to clarify vague data science requests.
Develop data-science-driven products that clinicians and teams can rely on, ensuring integration and usability.
Select appropriate analytical methods for decision-making, challenging weak designs and quantifying uncertainty.
Write maintainable code and integrate analytical models into applications, ensuring ongoing evaluation and improvement post-launch.
Enhance shared approaches for rapid model development and documentation, recognizing transferable solutions across domains.
Collaborate closely with domain experts in daily sessions, adapting to new evidence and communicating complex ideas clearly.
Utilize AI tools effectively while maintaining skepticism and judgment about their application.
Benefits
Comprehensive health insurance plans.
Retirement savings options with company matching.
Generous paid time off and holiday schedule.
Opportunities for professional development and training.
Flexible work arrangements to support work-life balance.
Full Job Description
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
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 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 above is preferred but not required 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 the design, prototyping, development, testing, deployment, and monitoring of statistical and 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 is ideal.
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.
About Hospital for Special Surgery
Hospital for Special Surgery (HSS) is a hospital in New York City that specializes in orthopedic surgery and the treatment of rheumatologic conditions. Founded in 1863 by James Knight, HSS is the oldest orthopedic hospital in the United States. The hospital has been ranked the top orthopedic hospital in the United States by U.S. News & World Report for 11 consecutive years. HSS has a staff of over 4000 employees, including more than 200 physicians and surgeons, and treats over 32,000 inpatients and 300,000 outpatients annually.
