A Data Scientist at Press ganey occupies a highly strategic and impactful role at the intersection of healthcare, data analytics, and patient experience. Press ganey is a pioneer in healthcare performance improvement, partner to more than 41,000 healthcare facilities. In this role, you are responsible for transforming complex, large-scale survey data—including patient satisfaction scores, clinical quality metrics, and nursing quality indicators—into actionable insights that help hospitals and healthcare systems improve patient care, safety, and operational efficiency.

The primary objective of the data science team is to extract meaningful patterns from structured and unstructured healthcare consumer feedback. This involves working extensively with survey methodologies, statistical analysis, and predictive modeling. Because Press ganey products directly influence clinical outcomes, patient safety, and executive decision-making across the healthcare industry, the models and analyses you build must be exceptionally rigorous, reproducible, and explainable.

You will typically collaborate with product managers, healthcare consultants, and engineering teams to integrate data-driven features into the company's core platform. Whether you are developing predictive models to identify patients at risk of readmission, analyzing key drivers of patient satisfaction (often measured on 1-5 Likert scales), or preparing research papers for publication, your work will directly impact how healthcare providers deliver care to millions of patients.

The questions you will encounter during the Press ganey interview process are designed to evaluate your technical competency, statistical foundations, and ability to translate data into business value. While specific questions will vary depending on the team and seniority level, they consistently focus on practical data manipulation, statistical interpretation, and communication.

Data Analysis & Survey Statistics

This category evaluates your understanding of statistical concepts, survey methodologies, and how to draw valid conclusions from structured survey data.

• How would you handle missing data or non-response bias in a large-scale patient experience survey?
• What statistical tests would you use to determine if a change in a hospital's patient satisfaction score (measured on a 1-5 Likert scale) is statistically significant?

Preparing for an interview at Press ganey requires a balanced approach that combines deep technical preparation with an understanding of healthcare metrics and stakeholder communication.

Role-Related Knowledge – You must demonstrate a strong command of statistical modeling, survey design, and data manipulation. Be prepared to discuss how to analyze structured survey data, specifically ordinal data like 1-5 rating scales. Familiarity with Python, SQL, and core machine learning concepts is essential.

Problem-Solving Ability – Interviewers want to see how you approach ambiguous data challenges. You should be able to take a high-level business or clinical question, translate it into a structured data analysis plan, execute the analysis, and derive actionable recommendations.

Communication & Presentation – Data scientists at Press ganey do not work in a vacuum. You will often need to present your findings to product managers, business leaders, or clients. Your ability to translate complex statistical concepts into simple, executive-ready language is highly valued.

Mission Alignment – Press ganey is deeply mission-driven, focusing on improving the safety, quality, and experience of healthcare. Demonstrating empathy, an understanding of the challenges facing healthcare providers, and a genuine interest in leveraging data to improve patient lives will set you apart.

The interview process for a Data Scientist at Press ganey typically spans several weeks and is designed to assess both your technical capabilities and your cultural fit. The process generally balances remote technical assessments with conversational interviews.

Initially, you will speak with a recruiter to discuss your background, career goals, and alignment with the company’s mission. Following this, you will typically have a conversation with the hiring manager or the head of the data science department. This stage focuses on your past experiences, your understanding of data science methodologies, and how you approach problem-solving.

If you progress, you will face technical evaluations. This often includes a technical phone or video screen focusing on SQL, Python, and statistical concepts. Many candidates are also given a take-home test assignment where they are asked to analyze a sample dataset, answer specific business questions, and prepare a presentation summarizing their findings. The final stages involve behavioral interviews and panels where you present your take-home assignment to team members and stakeholders.

The timeline above outlines the typical progression from the initial application to the final offer. Candidates should expect the entire process to take approximately three to six weeks, depending on scheduling availability and the depth of the take-home assignment evaluation. Use this timeline to pace your preparation, ensuring you allocate sufficient time to practice both your coding skills and your presentation delivery.

Survey Methodology & Statistical Modeling

A significant portion of Press ganey's core data comes from patient and employee surveys. Understanding how to handle, analyze, and model this specific type of data is critical for success in the interview.

Be ready to go over:

• Likert Scale Analysis – Understanding how to treat 1-5 ordinal data (e.g., when to use non-parametric tests versus parametric tests).
• Driver Analysis – Techniques such as multiple regression, random forests, or Shapley value analysis to determine which survey questions have the greatest impact on overall satisfaction.
• Sampling & Bias – How to account for response bias, small sample sizes in specific hospital units, and weighting survey responses to reflect the true patient population.
• Advanced concepts (less common) – Structural Equation Modeling (SEM), factor analysis for survey validation, and advanced survival analysis for clinical outcomes.

Example questions or scenarios:

• "How would you determine if a 0.2-point increase in a hospital's patient experience score is meaningful or just statistical noise?"
• "Describe how you would design a predictive model to identify which patients are most likely to leave a negative review."

Data Engineering & Pipeline Execution

Before you can model data, you must be able to query, clean, and structure it. The technical interview rounds will heavily test your ability to work with SQL and Python in a production-like environment.

Be ready to go over:

• SQL Proficiency – Writing complex queries involving window functions, CTEs (Common Table Expressions), and complex joins.
• Data Manipulation in Python – Using Pandas and NumPy to clean messy data, handle null values, and engineer features for machine learning models.
• Scalability – Designing queries and data pipelines that can efficiently process millions of survey responses without degrading performance.

Example questions or scenarios:

• "Write a Python script to identify and impute missing values in a dataset containing patient demographic data and survey responses."
• "How would you optimize a SQL query that is timing out when trying to aggregate patient scores across five years of historical data?"

Case Study & Presentation Delivery

The take-home assignment is a crucial component of the evaluation process. It tests your end-to-end data science workflow, from raw data to executive presentation.

Be ready to go over:

• Data Exploration – Conducting thorough exploratory data analysis (EDA) to find patterns and anomalies in the provided dataset.
• Business Translation – Answering the "so what?" of the data. You must connect statistical findings directly to business or clinical outcomes.
• Slide Design & Communication – Creating clean, professional slides that convey key insights clearly to both technical and non-technical audiences.

Example questions or scenarios:

• "Present your findings from the take-home dataset. What are the top three recommendations you would give to the hospital's Chief Medical Officer based on this data?"
• "How did you validate your model, and what are the limitations of the analysis you just presented?"