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

Expect a mix of technical deep-dives and behavioral inquiries. The questions are designed to see how you think under pressure and how you apply your academic training to real-world software problems.

Statistical and Domain Knowledge

• What are the primary assumptions of linear regression, and how do you test for them?
• Explain the concept of p-values to a non-statistician.
• How do you handle multi-collinearity in a high-dimensional dataset?
• Describe a time you had to deal with non-normally distributed data.
• What is the difference between a Type I and Type II error in the context of a clinical trial?

SAS Programming and Technical Skills

• What is the difference between a WHERE statement and an IF statement in a DATA step?
• How do you optimize a SAS program that is running slowly on a large dataset?
• Explain how the Program Data Vector (PDV) works during the execution phase.
• How would you use PROC TRANSPOSE to turn a wide file into a long file?
• Describe your experience with the SAS Macro facility.

Behavioral and Culture Fit

• Why SAS, and why this specific research group?
• Tell me about a time you had to work with a difficult team member.
• How do you stay current with the latest developments in statistics and data science?
• Describe a project where you had to learn a new tool or technique on a very tight deadline.
• What do you do when you realize you’ve made a mistake in your analysis after a project has been submitted?

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."

Behavioral and Collaborative Dynamics

SAS places a high premium on how you interact with a team. They want to ensure you can take direction from Research Scientists while also providing valuable feedback. This section evaluates your conflict resolution skills, your ability to meet deadlines, and your alignment with the company's long-term goals.

Be ready to go over:

• Project Management – How you prioritize tasks when supporting multiple research initiatives.
• Feedback Loops – Your experience receiving and implementing technical critiques of your work.
• Mission Alignment – Why you want to work at SAS specifically, beyond just the benefits and campus.

Example questions or scenarios:

• "Tell me about a time you found an error in a colleague's work. How did you handle the situation?"
• "Describe a situation where you had to explain a complex technical concept to a manager who was not a statistician."

Key Responsibilities

The day-to-day life of a Research Analyst at SAS is centered on supporting the creation and maintenance of world-class analytical software. You will spend your time conducting extensive data testing to ensure that the statistical procedures developed by the Research Scientists produce accurate results across a wide variety of edge cases. This involves writing complex SAS programs to simulate data, running benchmarks, and comparing outputs against established mathematical standards.

Collaboration is a constant feature of this role. You will regularly meet with managers and scientists to discuss research objectives and provide updates on data findings. You aren't just a "coder"; you are a validator and a consultant who helps ensure the software's integrity. Documentation is also a key component, as you will be responsible for detailing your testing processes and findings to ensure they meet the rigorous standards SAS is known for.

Beyond technical tasks, you will often contribute to the development of user documentation and examples. This helps customers understand how to apply SAS tools to their own research problems. Your work ensures that when a customer runs a procedure, they can trust the result implicitly, which is the foundation of the SAS brand.

Role Requirements & Qualifications

A successful Research Analyst candidate typically brings a strong academic background in a quantitative field such as Statistics, Biostatistics, Economics, or Mathematics. SAS values both theoretical depth and the practical ability to apply that knowledge to software development.

• Technical Skills – Proficiency in SAS programming (Base, Stat, Graph) is essential. Familiarity with SQL and other languages like R or Python is a significant plus, as SAS increasingly integrates with open-source tools.
• Experience Level – Most candidates have a Master's degree or PhD, though significant professional experience in a research-heavy role can be a substitute.
• Soft Skills – Excellent written and verbal communication skills are mandatory. You must be able to document research processes clearly and present findings to diverse internal teams.
• Must-have skills – Advanced knowledge of statistical distributions, hypothesis testing, and data cleaning techniques.
• Nice-to-have skills – Experience with SAS Viya, cloud computing environments, or specific industry knowledge (e.g., clinical trials or financial risk modeling).

Frequently Asked Questions

Q: How difficult is the Research Analyst interview at SAS? The difficulty is generally rated as average to high, depending on your statistical background. While the atmosphere is relaxed, the technical expectations are rigorous, and you will be interviewed by experts in the field.

Q: What is the typical timeline from application to offer? Most candidates hear back within a week of applying. The entire process, including the phone screen and on-site panel, usually takes between three to five weeks.

Q: Is there a specific "SAS style" of interviewing? Yes. It is centered on authenticity. Interviewers are not trying to "trick" you with brain teasers; they want to see your genuine problem-solving process and how you would fit into the team dynamic in Cary, NC.

Q: How much should I prepare for coding questions? You should be very comfortable writing SAS code on the fly. While you may not be asked to do "whiteboard coding" in the traditional software engineering sense, you will certainly be asked to explain code logic and syntax in detail.

Other General Tips

• Be Yourself: This is the most common advice from successful SAS candidates. The company values personality and honesty as much as technical skill.
• Know Your Research: Be prepared to discuss your thesis or past research projects in great detail. Your interviewers will likely be Research Scientists who will enjoy digging into the nuances of your work.
• Focus on the "Why": When answering technical questions, don't just give the answer. Explain the statistical theory that led you to that conclusion.
• Ask Thoughtful Questions: Use your time at the end of the interview to ask about the future of SAS software or the specific research challenges the team is currently facing.

Summary & Next Steps

The Research Analyst position at SAS is a prestigious role that offers the opportunity to contribute to the backbone of the analytics industry. By combining high-level statistical research with practical software validation, you will play a vital role in maintaining the company's reputation for excellence. The interview process, while technically demanding, is a reflection of the company's supportive and collaborative culture, particularly at the Cary, NC headquarters.

To succeed, focus your preparation on the fundamentals of statistical modeling, your proficiency in SAS programming, and your ability to communicate complex ideas clearly. Remember that SAS is looking for long-term colleagues, not just temporary contractors. Approaching the interview with honesty, curiosity, and a solid technical foundation will set you apart.

The compensation data above reflects the competitive nature of research roles at SAS. When evaluating an offer, consider the total package, including the world-class benefits and the stability of working for a global leader in analytics. For more detailed insights into specific team experiences and updated interview questions, continue your preparation on Dataford. Good luck—you are preparing for a career-defining opportunity.