Preparing for an interview at a world-class research institution requires a balanced approach. You must demonstrate both scientific rigor and practical engineering skills.

Scientific & Domain Expertise – You must show a strong grasp of how data science applies to biology or medicine. Be prepared to discuss clinical concepts, genomic data structures, or clinical trial design. Your interviewers will evaluate whether you understand the biological context of the data you are modeling.

Technical & Methodological Rigor – Expect your coding practices, statistical foundations, and machine learning workflows to be scrutinized. You need to demonstrate that you do not just run black-box models, but deeply understand the underlying assumptions, limitations, and evaluation metrics of your algorithms.

Communication & Collaboration – Because you will work closely with clinicians, biostatisticians, and laboratory technicians, your ability to simplify complex data science concepts is paramount. You will be evaluated on how clearly you present your ideas and how well you take feedback from multidisciplinary team members.

Mission Alignment – Working at MD Anderson Cancer Center requires a genuine commitment to oncology research and patient care. Interviewers want to see that you are motivated by the mission and possess the resilience required to tackle complex, ambiguous scientific problems.

The interview process for a Data Scientist at MD Anderson Cancer Center is highly thorough and typically spans several weeks to two months. Because many positions are tied directly to specific research labs or clinical departments, the process is often driven by the lab's Principal Investigator (PI) alongside institutional HR.

The process generally begins with either a direct outreach to a PI or a screening call with a knowledgeable recruiter who covers your background, technical tool stack, and interest in the role. This is followed by a structured multi-round evaluation designed to assess both your technical execution and your academic/scientific communication. A distinctive element of this process is the research presentation, where you will present your previous work to the entire lab group, simulating the collaborative environment you would join.

The timeline above details the typical progression from your initial contact through to the final decision. Candidates should use this timeline to pace their preparation, ensuring they have their research presentation polished and their technical foundations solid before entering the intensive mid-stage rounds. Depending on the specific lab and department, the exact sequencing of the technical assessment and panel presentation may vary slightly.

To succeed in this process, you must understand the specific competencies your interviewers are looking for during each interaction.

Research Presentation & Scientific Communication

This is often the most critical stage of the interview. You will be asked to present a past project or research paper to a panel of lab members, data scientists, and the PI.

Be ready to go over:

• Problem Formulation – How you defined the scientific question and translated it into a structured data science problem.
• Methodological Choices – A clear defense of why you chose specific models, statistical tests, or data preprocessing techniques over alternatives.
• Limitations and Future Work – An honest appraisal of your project's constraints, bias sources, and how you would improve the analysis with more resources.
• Advanced concepts (less common) – Integrating multi-omic data, handling batch effects in genomic datasets, and applying transfer learning to small clinical cohorts.

Example questions or scenarios:

• "Why did you choose a random forest over a simpler logistic regression for this clinical risk prediction task?"
• "How did you validate that your model's features were biologically meaningful and not just statistical noise?"

Core Data Science & Statistical Methods

You will face technical conversations or practical coding sessions with peer data scientists to evaluate your hands-on analytical capabilities.

Be ready to go over:

• Statistical Modeling – Survival analysis, hypothesis testing, regression diagnostics, and Bayesian inference.
• Machine Learning Pipelines – Feature engineering, cross-validation strategies for clinical data, and model interpretability (e.g., SHAP, LIME).
• Data Wrangling & Tooling – Efficient manipulation of large datasets using Python (Pandas, NumPy, Scikit-Learn) or R (tidyverse, Bioconductor).
• Advanced concepts (less common) – Graph neural networks for drug discovery, natural language processing for clinical notes, and deep learning for digital pathology.

Example questions or scenarios:

• "How would you design a cross-validation strategy for clinical data where multiple samples belong to the same patient?"
• "Explain how you would handle a dataset where 40% of the patient clinical markers are missing."

Lab Collaboration & PI Alignment

The final rounds focus heavily on your cultural fit, collaborative style, and alignment with the specific research goals of the lab.

Be ready to go over:

• Stakeholder Management – How you build trust with clinicians and translate their clinical hypotheses into data-driven experiments.
• Academic Rigor – Your familiarity with research writing, contributing to grant proposals, and maintaining clean, reproducible code repositories.
• Adaptability – Your comfort levels with ambiguity and shifting research priorities as new clinical trial data emerges.

Example questions or scenarios:

• "How do you handle a situation where a clinician wants to use a model that you know is statistically flawed or overfitted?"
• "Describe your experience working in multidisciplinary teams where members have very different levels of technical expertise."