Emory University Research Scientist Interview Guide 2026

What is a Research Scientist at Emory University?

The Research Scientist position at Emory University plays a pivotal role in advancing scientific inquiry and innovation within the university's research teams. As a Research Scientist, you will contribute to groundbreaking projects that span various fields, including biomedical research, epidemiology, and public health. This role is critical not only for the advancement of scientific knowledge but also for translating research findings into practical applications that can improve health outcomes in communities.

Your work will directly impact the university's mission to foster a collaborative environment that encourages interdisciplinary research and exploration. You will be part of a vibrant academic community, working alongside esteemed faculty, graduate students, and other researchers to tackle complex scientific questions. The position offers a unique opportunity to engage in high-impact research initiatives that influence both local and global health strategies, making it an exciting and rewarding career choice.

Through your contributions, you will help shape research directions, secure funding, and develop innovative methodologies that enhance the university’s reputation as a leader in research. If you are passionate about making a difference in the scientific landscape and eager to work in a supportive and intellectually stimulating environment, the Research Scientist role at Emory University could be the perfect fit for you.

Common Interview Questions

Expect interview questions to focus on your scientific expertise, research experiences, and collaboration skills. The questions listed below are representative of those you may encounter, derived from various candidate experiences. Remember, the goal is to familiarize yourself with the themes rather than memorize specific answers.

Technical / Domain Questions

This category assesses your depth of knowledge and expertise in your specific research area.

Explain your previous research and its significance.
How do you approach experimental design and methodology?
What techniques are you proficient in, and how have you applied them?
Describe a challenging problem you faced in your research and how you overcame it.
How do you stay current with advancements in your field?

Behavioral / Leadership Questions

Behavioral questions aim to evaluate your interpersonal skills, teamwork, and leadership capabilities.

Describe a time when you had to work with a difficult colleague. How did you handle it?
Can you provide an example of a successful collaboration that you led?
How do you prioritize your tasks when faced with multiple deadlines?
Discuss a situation where you had to adapt your research based on feedback.
What role do you typically take in a team setting?

Problem-Solving / Case Studies

These questions test your analytical thinking and problem-solving skills.

How would you approach a situation where experimental results contradict your hypothesis?
If given limited resources, how would you prioritize your research objectives?
Describe how you would design a study to test a specific hypothesis.
What steps would you take to troubleshoot an experiment that is not yielding expected results?
Discuss an ethical dilemma you faced in your research and how you resolved it.

See every interview question for this role

Practice questions from our question bank

Curated questions for Emory University from real interviews. Click any question to practice and review the answer.

Medium

NLP

Compare Positional Encodings in Transformers

Implement and compare sinusoidal vs learned positional encodings in a Transformer for legal clause classification where word order changes meaning.

Tokenization

Language Models

Deep Learning

Medium

Statistics & Probability

Root-Cause a Failing QC Assay

Use normal/t-tests and a lot-comparison Welch test to decide if a QC assay failure indicates a true mean shift or a bad reagent lot.

Hypothesis Testing

Statistical Significance

Experimentation

Hard

Model Evaluation

Evaluate MIMO Channel Estimation Errors

Assess how rising channel estimation error in a 4x4 MIMO system drives BER, outage, and throughput degradation, and recommend fixes.

Accuracy

Calibration

Easy

NLP

Classify RL-LLM Research Interests

Build a transformer-based multi-label classifier to categorize candidate statements about RL and LLM research interests into recruiting themes.

Tokenization

Language Models

Deep Learning

Medium

Machine Learning

Predict Turbine Failures from Sensor Data

Build a gradient-boosted classifier to predict turbine failures 7 days ahead from sensor, alarm, and maintenance history data.

Supervised Learning

Cross-Validation

Feature Engineering

Medium

Machine Learning

Rank Human Preferences for RLHF

Train a pairwise preference model for RLHF that predicts which LLM response humans prefer and produces deployable reward scores.

Supervised Learning

Regularization

Deep Learning

Medium

Machine Learning

Harden Vision Models Against Adversarial Attacks

Train a CNN for traffic-sign classification, measure adversarial robustness with FGSM, and improve deployed-model resilience with adversarial training.

Regularization

Neural Networks

Deep Learning

Hard

Machine Learning

Conditioned Reverse Diffusion Process for User Prompts

Develop a reverse diffusion model conditioned on user editing prompts for text generation tasks.

Neural Networks

Gradient Descent

Deep Learning

Easy

Coding

Explain Solution Time Complexity

Explain how to analyze a coding solution's time complexity by counting operations, loops, and data structure costs.

Arrays

Sorting

Searching

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Machine Learning

Build a Multimodal LLM for Real-Time Video Editing

Develop a multimodal large language model to enhance user experience in real-time video editing applications.

Supervised Learning

Deep Learning

Feature Engineering

Easy

Statistics & Probability

Explain P-Value in Marketing Test

Use a two-proportion z-test to assess a banner A/B test, then explain the resulting p-value clearly to a non-technical stakeholder.

Hypothesis Testing

Confidence Intervals

P-Values

Easy

Statistics & Probability

P-Value and Power for Button Redesign

Use a two-proportion z-test and power analysis to explain whether a 1-point signup lift from a button redesign is statistically credible.

Hypothesis Testing

P-Values

Power Analysis

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Model Evaluation

Choose RMSE or MAE

Explain RMSE vs MAE using two rent prediction models and recommend which metric and model better fit a business sensitive to large errors.

Regression

RMSE

MAE

Easy

Statistics & Probability

Confidence Interval for Email CTR Test

Construct and interpret a 95% confidence interval for the CTR lift in an email A/B test to communicate uncertainty in the experiment result.

Confidence Intervals

Statistical Significance

Experimentation

Easy

Statistics & Probability

Explaining P-Value and Power

Compute a two-proportion z-test and explain p-value and statistical power for an onboarding experiment with an inconclusive result.

Hypothesis Testing

P-Values

Power Analysis

Easy

Execution

Pivot FAIR Research for Reels Launch

Pivot a FAIR ranking project from Facebook Feed to Instagram Reels in 10 weeks under latency, fairness, and integration constraints.

Roadmapping

Trade-offs

Risk Assessment

Easy

Execution

Plan Research Team Matching Process

Run a 10-week pilot to improve Research Scientist team matching at Google across 8 teams, balancing speed, fairness, and research fit.

Trade-offs

Scope Management

Success Criteria

Easy

Model Evaluation

Alignment vs Evaluation Tradeoff

Explain alignment vs evaluation using a ChatGPT model release where safety improved but task completion, calibration, and over-refusal worsened.

Accuracy

Calibration

Threshold Tuning

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

Preparation is key to performing well in your interviews. Focus on understanding the expectations and evaluation criteria relevant to the Research Scientist position at Emory University.

Role-related knowledge – This refers to your technical skills and expertise in the scientific domain. Interviewers will evaluate your familiarity with research methodologies, data analysis, and relevant technologies. Be ready to demonstrate your depth of knowledge through examples from your previous work.

Problem-solving ability – Your approach to addressing complex scientific challenges will be scrutinized. Highlight your critical thinking skills and your ability to devise effective solutions in response to research inquiries.

Leadership – Your capacity to lead and influence others in a collaborative environment is vital. Showcase your experience in guiding teams, mentoring junior researchers, or spearheading projects that required coordination among multiple stakeholders.

Culture fit / values – Emory University values collaboration, innovation, and integrity. Be prepared to discuss how your personal values align with the university's mission and how you contribute to a positive team culture.

Interview Process Overview

The interview process for the Research Scientist position at Emory University typically follows a structured, multi-stage approach. You can expect an initial screening phase that may involve a brief phone interview or a preliminary discussion with a principal investigator (PI). Successful candidates will then progress to onsite interviews, which often include a presentation of your previous research, as well as meetings with faculty members and lab team members.

Throughout this process, the emphasis is on assessing both your technical skills and your fit within the collaborative research environment at Emory. Interviews may last several hours and involve a combination of formal presentations, one-on-one discussions, and group interactions. Expect a friendly yet rigorous atmosphere where interviewers are genuinely interested in understanding your background and how you can contribute to their research initiatives.

The visual timeline illustrates the stages of the interview process, from initial contact to final discussions. Use this timeline to plan your preparation accordingly and to manage your energy during the interview day. Each stage serves a purpose, and understanding the flow will help you focus your preparation efforts effectively.

Deep Dive into Evaluation Areas

In this section, we will explore key evaluation areas that are critical for success in the Research Scientist role at Emory University.

Role-related Knowledge

This area is crucial as it assesses your expertise in relevant scientific disciplines. Interviewers evaluate your familiarity with current research trends and methodologies. Strong performance includes demonstrating a deep understanding of your field and articulating how your knowledge applies to ongoing projects at Emory.

Research methodologies – Be prepared to discuss specific techniques you have used and their outcomes.
Data analysis – Show your proficiency in analyzing research data and interpreting results.
Literature review – Highlight your ability to engage with recent studies and how they inform your work.

Problem-solving Ability

Your capacity to devise solutions in research scenarios will be scrutinized. Interviewers look for structured problem-solving approaches and creative thinking. Strong candidates demonstrate a systematic method for addressing challenges and can articulate their thought processes clearly.

Experimental troubleshooting – Discuss how you handle unexpected results during experiments.
Research design – Be ready to explain how you would design a study to answer a specific research question.
Ethical considerations – Address how you navigate ethical dilemmas in research.

Leadership

As a Research Scientist, you need to exhibit leadership qualities, especially in collaborative settings. Interviewers will assess your ability to guide projects, mentor others, and foster teamwork.

Team collaboration – Provide examples of how you have successfully worked within a team.
Project management – Discuss your experience in leading research projects and coordinating with interdisciplinary teams.
Mentorship – Highlight any experience you have in mentoring junior researchers or students.

Advanced Concepts

While not always covered in detail, familiarity with these advanced topics can set you apart as a candidate:

Grant writing – Understanding the basics of securing research funding can be advantageous.
Interdisciplinary collaboration – Experience in cross-disciplinary research can be a differentiator.
Public engagement – Knowledge of how to communicate research findings to non-expert audiences.
"Describe a research project where you worked with collaborators from different fields."
"How do you approach writing grant proposals?"

Sign up to read the full guide

Create a free account to unlock the complete interview guide with all sections.

Interview Guides

Emory University

What is a Research Scientist at Emory University?

Common Interview Questions

Technical / Domain Questions

Behavioral / Leadership Questions

Problem-Solving / Case Studies

See every interview question for this role

Practice questions from our question bank

Sign up to see all questions

Getting Ready for Your Interviews

Interview Process Overview

Deep Dive into Evaluation Areas

Role-related Knowledge

Problem-solving Ability

Leadership

Advanced Concepts

Sign up to read the full guide

Key Responsibilities

Role Requirements & Qualifications

Frequently Asked Questions

Other General Tips

Tip

Summary & Next Steps