Preparation for the Data Scientist role at Medpace requires a structured approach that balances technical mastery with domain-specific knowledge. You should not expect a generic tech-industry coding interview; instead, focus on how data science supports clinical research and statistical reporting.

Biostatistical and Programming Rigor – You must demonstrate a strong foundation in statistics and hands-on proficiency in languages like SAS and R. Interviewers will evaluate your ability to write clean, reproducible code and your understanding of the mathematical assumptions behind statistical models.

Analytical Problem-Solving – You need to show how you approach complex, messy datasets. Candidates are evaluated on their ability to clean, transform, and structure clinical data logically while maintaining data integrity and regulatory compliance.

Scientific Communication – A key differentiator at Medpace is your ability to translate data into scientific insights. Through your interviews and your final presentation, you must show that you can clearly explain your methodology, defend your statistical choices, and present findings persuasively to both technical and non-technical panels.

CRO Alignment & Professionalism – Working in a clinical research organization requires high attention to detail, structured workflows, and a strong sense of professional responsibility. Interviewers look for candidates who are methodical, process-oriented, and genuinely motivated by clinical development.

The interview process for a Data Scientist at Medpace is thorough, highly structured, and designed to evaluate your technical competency, communication skills, and cultural fit. The process is characterized by multiple short, focused conversations rather than long, open-ended chats, reflecting the organized and methodical nature of clinical research.

The journey begins with an initial 30-minute phone screen, typically conducted by an HR representative or a member of the Biostatistics team. This round focuses on your resume, your basic programming background (particularly in SAS or R), and your motivation for joining Medpace. If you pass this screen, you will move on to the core interview stage, which consists of multiple back-to-back panel interviews and a final presentation.

During the panel stage, you will meet with junior, mid-level, and senior members of the Biostatistics and data science teams in separate 30-minute blocks. Each panel has a distinct focus, ranging from deep-dive technical questions to behavioral fit. The process culminates in a formal technical presentation where you present your past research or a significant data project to a panel of directors and senior team members, followed by a rigorous Q&A session.

The timeline above outlines the typical progression from your initial application to the final offer. Candidates should use this visual roadmap to pace their preparation, ensuring they allocate sufficient time to brush up on core statistics before the panel interviews and dedicate ample time to polishing their slide deck for the final presentation.

To stand out in the Medpace hiring loop, you must understand exactly what the interviewers are looking for in each core competency area.

Statistical Programming & SAS Proficiency

Because Medpace works extensively with clinical trial data destined for regulatory bodies like the FDA, SAS remains a cornerstone of their analytical pipeline, alongside R and Python. Your programming skills will be evaluated for efficiency, accuracy, and adherence to standard data structures.

Be ready to go over:

• Data Step Processing – Understanding how the program data vector (PDV) works, merging datasets, and filtering observations.
• Common Procedures – Proficiency with key procedures such as PROC SQL, PROC FREQ, PROC MEANS, and PROC TRANSPOSE.
• Macro Facility – How to write basic macros to automate repetitive data cleaning and reporting tasks.
• Advanced concepts (less common) – Understanding CDISC data standards, specifically SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model), which are industry standards for clinical trials.

Example questions or scenarios:

• "How would you transpose a wide dataset of patient vitals into a long format using SAS, and what are the potential pitfalls?"
• "Explain how you would write a macro to run the same statistical summary across multiple clinical trial sites."

Clinical Trial Methodology & Biostatistics

A Data Scientist at Medpace must understand the scientific context of the data they manipulate. You will be asked about statistical methods commonly used in clinical development.

Be ready to go over:

• Hypothesis Testing – Choosing the right statistical test (e.g., t-tests, ANOVA, Chi-square) based on data distribution and study design.
• Survival Analysis – Modeling time-to-event data, understanding censoring, and interpreting Kaplan-Meier curves.
• Handling Missing Data – Implementing techniques like multiple imputation or last observation carried forward (LOCF) and understanding their biases.
• Advanced concepts (less common) – Understanding adaptive clinical trial designs and covariate adjustment in clinical models.

Example questions or scenarios:

• "If a patient drops out of a clinical trial early, how does that affect your survival analysis, and how do you account for that censored data?"
• "Explain the difference between an Intent-to-Treat (ITT) analysis and a Per-Protocol (PP) analysis, and why both are important."

Technical Presentation & Communication

The final stage of the interview process requires you to give a technical presentation. This is your opportunity to showcase your communication skills, scientific depth, and ability to handle academic and professional scrutiny.

Be ready to go over:

• Project Structure – Organizing your presentation to clearly state the problem, the methodology, the results, and the business or clinical impact.
• Methodological Defense – Explaining why you chose specific models or statistical tests and acknowledging any limitations in your data or approach.
• Q&A Poise – Handling challenging questions from senior directors and biostatisticians with confidence and scientific humility.

Example questions or scenarios:

• "During your presentation, a senior director asks you to defend your choice of a random forest model over a simpler logistic regression. How do you justify your decision?"
• "How would you present these highly technical findings to a pharmaceutical client who does not have a background in data science?"