What is a Data Scientist at Tempus AI?

At Tempus AI, the role of a Data Scientist is pivotal to the company's mission of bringing the power of data and artificial intelligence to healthcare. You are not simply optimizing ad clicks or user retention; you are working with one of the world's largest libraries of clinical and molecular data to personalize patient care. Whether you are aligned with Real World Data Science, Translational Research, or Pharma R&D, your work directly bridges the gap between complex biological data and actionable medical insights.

You will join a team that operates at the intersection of technology and biology. Data Scientists at Tempus drive the development of algorithms that structure unstructured clinical notes, analyze genomic sequencing data, and generate Real World Evidence (RWE) to support pharmaceutical partners. You will tackle challenges ranging from cohort selection for clinical trials to building predictive models that determine which therapies might work best for a specific cancer patient.

This role requires a unique blend of technical rigor and domain curiosity. You will work alongside software engineers, pathologists, and bioinformaticians to productize your models. The impact of your work is tangible: the insights you generate help physicians make real-time decisions and accelerate the discovery of new therapeutics.

Common Interview Questions

Getting Ready for Your Interviews

Preparing for a Data Science interview at Tempus requires a shift in mindset. While standard machine learning proficiency is required, you must also demonstrate an aptitude for handling high-dimensional, messy, and biologically relevant data.

Key Evaluation Criteria:

Statistical and Machine Learning Depth – 2–3 sentences Interviewers will probe your understanding of the mathematical foundations behind the models you use. In the context of RWE and Translational Research, you must demonstrate expertise in areas like survival analysis, causal inference, and hypothesis testing, rather than just black-box deep learning.

Data Wrangling and Fluency – 2–3 sentences Healthcare data is notoriously unstructured and complex. You will be evaluated on your ability to manipulate data using SQL and Python (pandas), specifically your ability to clean, join, and make sense of disparate datasets like electronic medical records (EMR) and genomic panels.

Product and Scientific Communication – 2–3 sentences You must be able to translate complex statistical findings into clear narratives for non-technical stakeholders, such as clinicians or pharma clients. Tempus values candidates who can explain why a model works and what the clinical implications of a false positive might be.

Mission Alignment and Culture – 2–3 sentences Tempus is fast-paced and mission-driven. You will be assessed on your passion for precision medicine and your ability to thrive in an environment that combines the rigor of academia with the speed of a technology startup.

Interview Process Overview

The interview process at Tempus AI is designed to test both your technical capabilities and your ability to apply them to healthcare-specific problems. Generally, the process moves quickly, reflecting the company's startup roots. It typically begins with a recruiter screen to assess your background and interest in the intersection of AI and healthcare, followed by a technical screen.

The technical screen often involves a live coding session or a take-home challenge focused on practical data manipulation and analysis. Unlike general tech companies that focus heavily on algorithmic puzzles (LeetCode), Tempus tends to prioritize practical data science tasks—cleaning a dataset, performing exploratory analysis, and building a baseline model. Following this, the onsite stage (virtual or in-person) consists of a series of panels covering technical depth, case studies, and behavioral fit.

Throughout the process, expect a collaborative atmosphere. Interviewers are looking for problem solvers who can navigate ambiguity. You will likely speak with cross-functional team members, including other data scientists, engineers, and potentially domain experts like computational biologists, to ensure you can communicate effectively across disciplines.

This timeline outlines the typical flow from application to offer. Use this to gauge where you are in the pipeline and prepare your energy for the intensive onsite loop. Note that the specific technical assessments may vary slightly depending on whether you are interviewing for the RWE or Translational Research teams.

Deep Dive into Evaluation Areas

To succeed, you need to prepare for specific technical domains that are heavily emphasized at Tempus. Based on candidate reports and job requirements, the following areas are critical.

Statistical Modeling & Machine Learning

This is the core of the interview. You need to show that you understand the "under the hood" mechanics of algorithms, not just how to import them. Because Tempus deals with patient lives, interpretability and robustness are often more important than raw prediction accuracy.

Be ready to go over:

• Classical Machine Learning – Random Forests, Gradient Boosting (XGBoost/LightGBM), and Logistic Regression are workhorses here. Know their hyperparameters and failure modes.
• Survival Analysis – Kaplan-Meier curves and Cox Proportional Hazards models are essential for oncology data.
• Causal Inference – Understanding the difference between correlation and causation, especially in observational studies (RWE).
• Advanced concepts – Bayesian statistics, dimensionality reduction (PCA/t-SNE for genomic data), and NLP (for processing clinical notes).

Example questions or scenarios:

• "How would you handle class imbalance in a dataset where the positive outcome (e.g., a specific rare mutation) is less than 1%?"
• "Explain the difference between L1 and L2 regularization and when you would use each."
• "How do you validate a model when your training data has a different distribution than your real-world test data?"

Coding & Data Manipulation

Expect practical coding questions. You will likely be asked to manipulate data in Python or SQL. The focus is on data intuition—how you handle missing values, duplicates, and messy strings.

Be ready to go over:

• Pandas Proficiency – Groupby, pivot tables, merging dataframes, and handling datetime objects.
• SQL Complexity – Writing queries involving multiple joins, window functions, and CTEs to extract cohorts from a database.
• Data Cleaning – Strategies for imputing missing clinical data (e.g., missing lab vitals).

Example questions or scenarios:

• "Given a dataset of patient visits, write a query to find the time elapsed between the first and second visit for each patient."
• "Here is a CSV with messy clinical notes. Write a Python script to parse out the medication names and dosages."
• "How would you optimize a slow SQL query running on a table with billions of rows?"

Case Studies & Product Sense

These interviews test your ability to translate a vague business or clinical problem into a data science problem. You will be given a scenario relevant to Tempus's work and asked to design a solution end-to-end.

Be ready to go over:

• Metric Definition – Choosing the right metric for success (e.g., F1-score vs. Accuracy in medical diagnosis).
• Study Design – How to select a control arm for a study using retrospective data.
• Feasibility – Assessing whether the data available is sufficient to answer the question asked.

Example questions or scenarios:

• "A pharma partner wants to know if Drug A works better than Drug B for a specific lung cancer subtype. How do you design this analysis using our database?"
• "We want to build a model to predict sepsis in ICU patients. What features would you use, and how would you structure the target variable?"