What is a Research Analyst at SAS?

A Research Analyst at SAS is a cornerstone of the company’s mission to transform data into intelligence. In this role, you are not simply performing data entry or basic reporting; you are part of an elite team that bridges the gap between theoretical statistical research and functional software solutions. You will work alongside some of the world’s leading Research Scientists to validate methodologies, refine algorithms, and ensure that SAS products remain the gold standard for analytical excellence across global industries.

Your impact is felt directly by the millions of users who rely on SAS for critical decision-making in healthcare, finance, and government. Whether you are assisting in the development of new procedures for SAS/STAT or optimizing data workflows for SAS Viya, your work ensures the reliability and accuracy of the world’s most sophisticated analytics platform. This position requires a unique blend of mathematical curiosity and technical discipline, making it one of the most intellectually stimulating paths within the organization.

The role is primarily based at the SAS global headquarters in Cary, NC, an environment designed to foster innovation through collaboration and a healthy work-life balance. As a Research Analyst, you are expected to be a subject matter expert who can translate complex data findings into actionable insights for product managers and developers, ensuring that the software remains both powerful and user-friendly.

Common Interview Questions

Getting Ready for Your Interviews

Preparing for an interview at SAS requires a dual focus: demonstrating deep technical proficiency in statistical methods and showcasing a personality that thrives in a collaborative, research-driven environment. The company values candidates who are lifelong learners and who approach problems with both a scientific mindset and a practical eye for software application.

Analytical Rigor – This is the most critical criterion. Interviewers will evaluate your ability to select the correct statistical models for specific data types and your understanding of the underlying assumptions. You should be prepared to explain the "why" behind your methodology, not just the "how."

Technical Proficiency – You must demonstrate a high level of comfort with data manipulation and programming. While SAS software is the primary tool, the ability to think algorithmically and handle large, messy datasets is what interviewers are truly looking for. Strength in this area is shown by writing clean, efficient code and explaining your logic clearly.

Communication and Collaboration – Because you will work closely with Research Scientists and managers, your ability to distill complex findings into understandable language is vital. Interviewers look for candidates who listen actively, ask clarifying questions, and can defend their research choices without being defensive.

Culture Fit and Values – SAS is famous for its "people-first" culture. They look for authenticity, honesty, and a genuine passion for the field. You can demonstrate this by being yourself, showing curiosity about the company's long-term vision, and illustrating how you navigate ambiguity in research projects.

Interview Process Overview

The interview process for a Research Analyst at SAS is designed to be thorough yet welcoming. It typically begins with an online application followed by a screening process that focuses on your academic background and technical interests. Unlike many high-pressure tech environments, SAS aims to make candidates feel comfortable, believing that people perform best when they feel welcomed and respected.

The pace is generally steady, with the entire process often concluding within a few weeks. You can expect a heavy emphasis on your previous research experience and your ability to work within a team of specialists. The company’s philosophy is to find the right long-term fit, so they invest time in letting you meet various stakeholders, from direct managers to the scientists you will be supporting daily.

The timeline above illustrates the progression from your initial conversation with the hiring manager to the comprehensive on-site panel. Candidates should use this to pace their preparation, focusing on high-level experience during the phone screen and deep-diving into technical methodology for the on-site rounds. While the process is described as "relaxed," the depth of questions during the on-site stage remains rigorous.

Deep Dive into Evaluation Areas

Statistical Methodology and Research Design

This area is the heart of the Research Analyst role. Interviewers want to see that you have a fundamental grasp of statistics that goes beyond software syntax. They will probe your understanding of experimental design, hypothesis testing, and various modeling techniques (such as regression, ANOVA, or mixed models).

Be ready to go over:

• Model Selection – How to choose the right statistical approach based on the research question and data constraints.
• Assumptions and Validations – Identifying when a model's assumptions are violated and how to correct for it.
• Data Integrity – Techniques for handling missing data, outliers, and sampling bias in a research context.

Example questions or scenarios:

• "Walk us through a research project where you had to pivot your methodology due to unexpected data limitations."
• "How would you explain the difference between a fixed effect and a random effect to a non-technical stakeholder?"

SAS Programming and Data Management

As a Research Analyst, you will spend a significant portion of your time in the code. Performance is evaluated based on your ability to manipulate data efficiently and your familiarity with the SAS ecosystem. They are looking for "SAS-native" thinking—using procedures (PROCs) effectively rather than relying on inefficient loops.

Be ready to go over:

• DATA Step Processing – Understanding the program data vector (PDV) and efficient data merging.
• Macro Facility – Using macros to automate repetitive research tasks and create dynamic code.
• Output Delivery System (ODS) – Generating professional reports and graphics from research findings.
• Advanced concepts – Proc SQL optimization, hash objects for large data lookups, and integration with SAS Viya.

Example questions or scenarios:

• "Given a large dataset with nested observations, how would you restructure it for a longitudinal analysis?"
• "Explain how you would use the SAS Macro facility to run a simulation 1,000 times and aggregate the results."