A Data Analyst at Tempus labs plays a pivotal role in bridging the gap between raw clinical data and groundbreaking precision medicine. Tempus labs leverages an interactive platform to analyze clinical and molecular data, providing genomic profiling and therapeutic insights to oncology clinics, research institutions, and pharmaceutical partners. In this role, you are responsible for transforming complex, unstructured medical records into clean, structured, and highly accurate datasets that directly power AI-driven clinical decision support tools.

Your work has a tangible impact on cancer research and patient outcomes. Whether you are cataloging oncology patient symptoms, mapping clinical trial criteria, or validating molecular databases, your analytical precision ensures that physicians receive reliable data when making critical treatment decisions. This is not a typical corporate analytics role; it requires a deep curiosity about healthcare, biological systems, and clinical workflows, combined with a rigorous approach to data integrity.

Because Tempus labs operates at the cutting edge of biotech, the data pipelines you manage are both massive and highly complex. You will collaborate closely with Clinical Data Managers, genomic scientists, and software engineers to optimize data ingestion workflows. It is an intellectually demanding environment where your data manipulation skills contribute directly to the broader mission of personalizing healthcare at scale.

Interview questions for the Data Analyst role at Tempus labs are designed to evaluate your clinical data comprehension, technical proficiency, and resilience when managing high-volume data tasks. The questions below represent patterns identified from real candidate experiences and are grouped to help you structure your preparation.

Clinical Data & Domain Knowledge

These questions assess your ability to interpret medical records, understand clinical terminology, and structure complex patient data.

• How would you approach cataloging highly unstructured clinical records for oncology patients?
• What challenges do you anticipate when mapping patient symptoms from free-text doctor notes into structured database fields?

Preparing for an interview at Tempus labs requires a strategic focus on both clinical data awareness and technical execution. The interview team looks for candidates who can demonstrate a high level of precision, as errors in clinical data can have serious downstream consequences for research and patient care.

Clinical Data Acumen – You must demonstrate a strong comfort level with medical terminology, particularly in oncology and genomics. Interviewers will evaluate your ability to read, interpret, and logically organize unstructured clinical narratives into clean, standardized datasets.

Data Manipulation & Quality Assurance – You need to show that you can work efficiently with tools like Excel, SQL, or Python to clean and validate data. You will be evaluated on your ability to spot inconsistencies, handle missing values, and build reproducible data preparation workflows.

Resilience & Focus – Clinical data curation can involve high-volume, repetitive tasks that require sustained concentration. You should demonstrate a strong work ethic, attention to detail, and a clear understanding of how repetitive cataloging tasks contribute to the company's broader mission.

Mission Alignment – Tempus labs is deeply mission-driven. You must be prepared to articulate why you want to work at the intersection of technology and healthcare, and how your background aligns with the goal of advancing precision medicine.

The interview process for a Data Analyst at Tempus labs is designed to evaluate both your technical capabilities and your alignment with the day-to-day realities of clinical data curation. While the process is structured, candidates often experience a series of conversational and highly practical evaluation stages.

The journey typically begins with a screening call from a recruiter to discuss your background, interest in the company, and basic qualifications. Following this, you will have a 30-minute interview with the hiring manager or a senior team member to dive deeper into your experience and assess your understanding of the healthcare data space.

A defining feature of the Tempus labs process is the clinical data take-home project. This assessment simulates the actual day-to-day responsibilities of the role, often requiring you to catalog sample cancer patient data within a specific timeframe. If your submission meets their standards, you will advance to the final round, which typically consists of a series of back-to-back interviews with clinical data managers, team members, and directors.

The visual timeline above outlines the typical progression from your initial application to the final offer stage. Candidates should use this timeline to plan their preparation, ensuring they allocate sufficient time to complete the intensive take-home project and prepare for multiple peer interviews in the final round.

To succeed in the Tempus labs interview process, you must perform exceptionally well across several core evaluation areas. Understanding what the interviewers are looking for in each area will help you structure your preparation.

Clinical Data Cataloging & Structuring

This is the core of the Data Analyst role. Interviewers want to see how you handle real-world, messy clinical data and translate it into a structured format that can be easily queried and analyzed.

Be ready to go over:

• Oncology Terminology – Basic understanding of cancer stages, treatment types (chemotherapy, immunotherapy), and pathology reports.
• Data Standardization – How to map variable medical descriptions to standardized clinical codes or categories.
• Data Entry Best Practices – Methods for ensuring high accuracy and zero typos when processing patient records manually or semi-automatically.

Example scenarios:

• You are given a raw doctor's note describing a patient's response to a specific cancer therapy. How do you extract and input this information into a structured spreadsheet?
• How do you handle conflicting information within a patient's medical history file?

Technical Data Manipulation

While this is often an entry-to-mid-level role, technical competence with data tools is highly valued. You must prove you can manipulate data efficiently without relying solely on manual processes.

Be ready to go over:

• Advanced Excel – Mastery of VLOOKUP/XLOOKUP, index-match, pivot tables, and data validation rules.
• SQL Basics – Writing select statements, filtering data, joining tables, and aggregating clinical metrics.
• Python or R (Optional but highly valued) – Using libraries like Pandas to automate data cleaning and transformation tasks.

Example scenarios:

• You have two separate datasets of patient records from different clinics. How do you merge them while ensuring no duplicate patient profiles are created?
• Walk me through how you would write a SQL query to find all patients diagnosed with a specific condition who are over the age of 50.

Behavioral & Operational Fit

Because the work can be repetitive and requires high focus, interviewers pay close attention to your work ethic, motivation, and ability to handle high-volume pipelines.

Be ready to go over:

• Handling Repetitive Tasks – Strategies for maintaining enthusiasm and attention to detail during long periods of data entry or cataloging.
• Time Management – Navigating tight deadlines and prioritizing high-volume data queues.
• Communication – Explaining data discrepancies clearly to cross-functional stakeholders.

Example scenarios:

• Tell me about a time you had to complete a large volume of data-related tasks that were highly repetitive. How did you ensure your quality did not slip?
• How do you handle a situation where a manager sets an aggressive deadline for a data curation project?