Data Scientist

Hospital for Special Surgery•New York, NY

2d•$101,500 - $172,500

About The Position

Hospital for Special Surgery (HSS) is hiring a Data Scientist to identify meaningful opportunities with domain experts and turn ambiguous questions into well-defined problems tied to decisions, workflows, and measurable impact. The role delivers data-science-driven features through applications and products used by clinical, operational, research, finance, and executive teams, rather than handing off stand-alone analyses or notebooks. The Data Scientist selects appropriate analytical methods, including forecasting, predictive modeling, experimentation, causal analysis, segmentation, anomaly detection, risk stratification, and measurement design. The role develops and productionizes maintainable code and integrates models and analytical logic into applications and Databricks workflows. Responsibilities span design, prototyping, development, testing, deployment, integration, evaluation, monitoring, and continued iteration, as well as building reusable practices for model development, pipelines, documentation, monitoring, and reproducibility. The position involves close, synchronous collaboration with domain experts, communicating quantitative ideas to varied audiences, and using AI agentic coding tools with sound judgment and skepticism. HSS describes its workplace as innovative, supportive, and inclusive, and emphasizes opportunities for growth and success. This is a regular full-time, day-shift position. The stated base pay scale is $101,500 to $172,500; the finalist’s salary will depend on factors including role scope, experience, education, accomplishments, internal equity, budget, and fair-market-value evaluation.

Requirements

Bachelor’s degree in a quantitative field or equivalent demonstrated technical ability.
Strong foundations in data science, statistics, and modeling, including the ability to explain and defend methodological choices, recognize leakage and confounding, quantify uncertainty, and select problem-appropriate performance metrics.
Strong programming ability, such as Python and SQL, and comfort with git, application development, and cloud or lakehouse environments.
Experience with projects involving large datasets and the design, prototyping, development, testing, deployment, and monitoring of statistical and machine-learning models.
Experience in at least one of forecasting, experimentation, causal inference, anomaly detection, risk stratification, optimization, or measurement design.
Experience with modern AI agentic coding tools and techniques, including the judgment to decide what to delegate, verify results, and understand models’ strengths and weaknesses.
Ability to structure messy problems, exercise judgment about assumptions and unknowns, and determine what matters to the problem.
Flexibility, communication, collaboration, comfort with ambiguity, and ability to ramp up quickly and work effectively with domain experts who are not data experts.
Drive, vision, bias toward action, and ownership.
For Data Scientist I: generally 0–2 years of professional data science or advanced analytics experience; graduate, internship, fellowship, research, or substantial applied-project work may count. Candidates should have exposure to end-to-end production work or be ready to learn it quickly.
For Data Scientist II: generally 3–5+ years of professional data science or advanced analytics experience, with substantial end-to-end production experience and a record of independently leading work from problem discovery through deployment, integration, monitoring, and iteration. Ability to operate autonomously, lead through influence, juggle priorities, mentor others, and connect work across domains is particularly expected.

Nice To Haves

Master’s degree or higher; preferred but not required for Data Scientist II roles.
Prior Databricks experience.
Experience building data-science-driven product features or decision-support tools that progressed from problem ideation to live application use, monitoring, and iteration.
Experience with healthcare data in a U.S.-based healthcare delivery or health-tech setting, especially musculoskeletal health and orthopedics.
Experience in operations, finance, economics, research, clinical science, consumer or digital products, or other complex enterprise settings.

Responsibilities

Work with domain experts to identify valuable opportunities and frame vague questions as well-defined problems with explicit assumptions, affected decisions or workflows, and credible measures of impact and value.
Build and deliver data-science-driven features in applications and products that users can rely on, contributing across design, prototyping, development, testing, deployment, integration, monitoring, and iteration.
Choose and apply suitable analytical methods, such as predictive modeling, forecasting, experimentation and A/B testing, causal or program-effectiveness analysis, segmentation, anomaly detection, risk stratification, and measurement design.
Challenge weak analytical designs, quantify uncertainty, and select methods and performance metrics appropriate to the problem.
Write maintainable code and integrate models and analytical logic into applications and Databricks workflows.
Develop appropriate evaluation and monitoring, respond to model drift or failures, and improve products after launch.
Improve reusable approaches for model development, feature and evaluation pipelines, model cards, monitoring, documentation, and reproducibility.
Identify methods, datasets, features, or workflows that can be reused across domains.
Collaborate in daily synchronous working sessions with domain experts; ask questions, challenge assumptions, adapt to new evidence, and explain quantitative ideas clearly to different audiences.
Use agentic coding tools effectively while critically checking their output and challenging implicit assumptions.