To succeed in your interviews, you must understand exactly how Analysis Group assesses candidates across different competencies. The evaluation is rigorous and highly specific to the demands of economic and healthcare consulting.
Data Manipulation and Programming
This is the core technical requirement of the HEOR Data Programmer role. Interviewers want to see that you can take raw, unstructured, or massive datasets and transform them into clean, analyzable formats. Strong performance means writing code that is not only correct but also readable, reproducible, and well-documented.
Be ready to go over:
- Data Wrangling – Filtering, merging, joining, and aggregating large datasets using SQL, R (dplyr/tidyverse), Python (pandas), or SAS.
- Handling Missing Data – Identifying, imputing, or safely excluding missing values without compromising the dataset's integrity.
- Code Optimization – Writing efficient queries that do not consume excessive memory, which is critical when dealing with millions of rows of healthcare claims.
- Advanced concepts (less common) – Writing macros or custom functions to automate repetitive data cleaning tasks; basic version control (Git) practices.
Example questions or scenarios:
- "Walk me through how you would join two large datasets where the primary keys do not perfectly match."
- "How do you handle duplicates and missing values in a dataset containing patient records?"
- "Explain a time you had to optimize a slow-running SQL query or R script. What steps did you take?"
Statistical Analysis and Implementation
While you are not expected to be a PhD-level biostatistician, you must understand the math behind the code you write. You will be evaluated on your ability to implement statistical methodologies under the guidance of senior staff. Strong candidates can explain the "why" behind their analytical choices.
Be ready to go over:
- Descriptive Statistics – Calculating means, medians, variances, and standard deviations, and knowing when to use each.
- Regression Analysis – Understanding the assumptions behind linear and logistic regression and how to prepare data for these models.
- Data Visualization – Developing clear, accurate tables and figures to support study deliverables.
- Advanced concepts (less common) – Survival analysis concepts (Kaplan-Meier curves) or propensity score matching, which are frequently used in HEOR.
Example questions or scenarios:
- "How would you write a program to generate a summary table of patient demographics (e.g., age, gender, baseline comorbidities)?"
- "Explain the difference between linear and logistic regression. When would you use one over the other?"
- "If your regression model outputs an unexpected result, how do you go about troubleshooting the data inputs?"
Quality Assurance and Attention to Detail
In the life sciences consulting space, data accuracy is non-negotiable. Interviewers will heavily scrutinize your approach to quality control. A strong candidate actively anticipates edge cases, writes defensive code, and builds validation checks into their programming workflow.
Be ready to go over:
- Data Validation – Checking for logical inconsistencies (e.g., a patient receiving a treatment before they were diagnosed).
- Code Review – Documenting your steps clearly so that another programmer can audit your work.
- Reconciliation – Comparing your output against known benchmarks or control totals to ensure data hasn't been lost during joins.
Example questions or scenarios:
- "Tell me about a time you discovered a significant error in your own code or data. How did you fix it and prevent it from happening again?"
- "What is your step-by-step process for QAing a dataset before handing it over to a senior analyst?"
- "How do you ensure your code is readable and maintainable for someone who might take over your project six months from now?"
Behavioral and Consulting Fit
Analysis Group prides itself on a culture of transparency, trust, and respect. You will be evaluated on your interpersonal skills, your eagerness to learn, and your ability to thrive in a fast-paced, client-driven environment.
Be ready to go over:
- Team Collaboration – Working effectively with cross-functional teams, including economists, epidemiologists, and project managers.
- Time Management – Balancing multiple project deadlines simultaneously.
- Communication – Translating complex programming challenges into plain language for non-technical team members.
Example questions or scenarios:
- "Describe a time you had to explain a complex technical issue to a non-technical stakeholder."
- "How do you prioritize your tasks when you have conflicting deadlines from two different project managers?"
- "Tell me about a time you had to learn a new tool or programming language on the fly to complete a project."
