Veeva Systems Data Scientist Interview Questions + Guide 2024

With a fiscal year 2024 revenue of over $2K million, Veeva Systems is the leading cloud-based software solution provider for the global life science industry. It offers a comprehensive suite of software applications to address the unique needs of life science companies, including CRM, content management, clinical trial management, regulatory compliance, and commercial operations.

Veeva Systems relies on data science to collect, clean, and analyze large datasets from various sources, including clinical trials, sales data, and market research, to make informed decisions about drug development, commercialization, and regulatory compliance. Data scientists at Veeva also strive to build predictive models to forecast future trends, identify potential risks, and optimize business processes.

If you have the opportunity to interview for a data scientist position at Veeva Systems, you’re in the right place to prepare. In this article, we’ll cover how Veeva Systems conducts data scientist interviews and the types of questions you can expect.

Veeva Systems is known for its rigorous and comprehensive interview process for data scientist roles. The process typically involves multiple rounds, each designed to assess different aspects of your skills and qualifications. Here is a brief breakdown of what to expect:

Initial Phone Interview

After your application is reviewed for relevant experience, skills, and alignment with the job requirements, you’ll be asked to hop into a quick call with a recruiter. This is usually a brief screening call to discuss your background, interest in the role, and availability for further interviews. It’s also an opportunity for you to clear any doubts, if you have, about the role and the company.

Technical Assessment Rounds

Further into the interview process, you may be asked to complete a coding challenge online or in person. This could involve solving data structures and algorithms problems or working on a data science-related task using Python or R, whatever you choose.

You’ll be presented with a real-world data science problem and asked to propose a solution, including data cleaning, analysis, and modeling techniques. You may also be asked to explain your solution to this case-study problem to the interviewers in the on-site technical rounds.

On-Site Interview Rounds

If you successfully cover the previous rounds, you’ll be invited for an on-campus or virtual on-site interview loop. While it mainly encompasses technical aspects of data science roles, you’ll likely have several behavioral interviews with different team members. Expect questions about your past experiences, problem-solving skills, teamwork abilities, and how you approach data-driven decision-making.

The technical interview rounds will focus on your expertise in specific data science concepts, machine learning algorithms, and statistical methods. Moreover, be prepared to discuss your previous data science projects in detail, including your role, challenges faced, and outcomes.

Hiring Manager Interview

Often included in the on-site loop, this final interview often involves the hiring manager to assess if you’re a good fit for the team and company culture. Expect to discuss your understanding of the role, expectations, and any remaining questions you may have.

Here are some Veeva Systems data scientist questions that may be asked during the interview:

1. What are your three biggest strengths and weaknesses you have identified in yourself?

2. How would you convey insights and the methods you use to a non-technical audience?

3. How comfortable are you presenting your insights?

4. Tell me about a project where you had to clean and organize a large dataset.

5. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

6. How would you build a job recommendation feed?

7. How would you explain the bias-variance tradeoff when building and selecting a model for loan approvals?

8. Write a function named grades_colors to select only the rows where the student’s favorite color is green or red and their grade is above 90.

9. How would you build the recommendation algorithm for type-ahead search for Netflix?

10. Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half, if there is no index where this condition is satisfied return -1.

11. Write a function that performs bootstrap sampling on the given array and calculates the confidence interval based on the given size.

12. How would you create a system that automatically detects if a listing on the marketplace sells a gun?

13. Jetco claims the fastest average boarding times in a recent study. What factors could have biased this result, and what would you investigate?

14. Write a function friendship_timeline to generate an output that lists the pairs of friends with their corresponding timestamps of the friendship beginning and then the timestamp of the friendship ending.

15. Without using the pandas package, write a function read_split_from_str to split the data into two lists, one for training and one for testing, with a 70:30 split between the training set and the testing set.

16. Given that it is raining today and rained yesterday, write a function to compute the probability of rain on the nth day after today.

17. Let’s say that you’re a data scientist at Robinhood. How do we measure the launch of Robinhood’s fractional shares program?

18. How would you design a machine learning model to predict the likelihood of a clinical trial’s success based on historical data, including patient demographics, trial design parameters, and interim analysis results? Consider the ethical implications of such a model.

19. Given a dataset of patient medical records, genomic data, and drug response information, how would you approach building a predictive model to identify patients most likely to benefit from a particular drug? What challenges might you encounter in terms of data quality, bias, and interpretability?

20. How would you develop a computational model to identify potential drug repurposing opportunities? What factors would you consider, and what challenges might arise in validating such predictions?

Preparing for a data scientist interview at Veeva Systems involves several key steps. Here’s a comprehensive guide to help you get ready:

Understand Veeva Systems and Its Products

Acquaint yourself with Veeva Systems, its history, mission, and position within the life sciences industry, and its product offerings, particularly the cloud-based software solutions for CRM and content management. Gain insights into how Veeva’s solutions are used by life sciences companies. Knowing how data science contributes to these areas will be advantageous for your interview.

Review Core Data Science Concepts

Expect questions on statistical methods, probability distributions, hypothesis testing, and A/B testing during the Veeva Systems data scientist interview. Review common algorithms like linear regression, decision trees, clustering, and neural networks. Understand how they work, their applications, and their limitations.

Moreover, know how to efficiently clean, normalize, and preprocess data. This includes handling missing data, feature selection, and dimensionality reduction for data science applications. Also, brush up on your coding skills in SQL, Python, and R, focusing on libraries like pandas and NumPy.

Practice Technical Skills

It’s not enough to only brush up on the programming concepts to crack Veeva Systems data scientist roles. Practice solving coding problems, focusing on algorithms, data structures, and data manipulation. Work on case studies or project interviews that involve analyzing real datasets available on platforms like Kaggle. Be also prepared to discuss your approach to data exploration, feature engineering, and model selection.

Since data retrieval is a critical part of a data scientist’s role, ensure you can write complex SQL queries to extract and manipulate data.

Prepare for Behavioral and Case Interviews

Veeva Systems emphasizes practical skills and experience. Prepare behavioral questions and product sense questions that involve solving a specific business problem using data science. This could include designing an experiment, predicting an outcome, or optimizing a process.

Participate in Mock Interviews

Conduct mock interviews to simulate the real interview experience through our P2P Mock Interview Portal and AI Interviewer. This will help you refine your answers and improve your confidence.

What other companies are hiring Data Scientists besides Veeva Systems?

Numerous companies are hiring Data Scientists across various industries. Some well-known examples include Google, JPMorgan Chase, and Amazon.

Does Interview Query have job postings for the Veeva Systems Data Scientist role?

Yes, we have job postings for the Veeva System Data Scientists role. You can explore our Job Board to see the current job posts for Veeva Systems Data Scientist.

The Veeva Systems Data Scientist interview process is rigorous, focusing on technical skills, problem-solving abilities, and industry knowledge. By understanding the key areas assessed and preparing accordingly, you can increase your chances of success. In addition to Data Scientists, Veeva offers a variety of other roles within the life sciences industry, including Software Engineer, Data Analyst, and Product Manager.