Cognizant Technology Solutions Data Scientist Interview Guide
Overview
Getting ready for an Data Scientist interview at Cognizant Technology Solutions? The Cognizant Technology Solutions Data Scientist interview span across 10 to 12 different question topics. In preparing for the interview:
- Know what skills are necessary for Cognizant Technology Solutions Data Scientist roles.
- Gain insights into the Data Scientist interview process at Cognizant Technology Solutions.
- Practice real Cognizant Technology Solutions Data Scientist interview questions.
Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Cognizant Technology Solutions Data Scientist interview.
Cognizant Technology Solutions Data Scientist Salary
Cultural and Behavioral Questions
This feature is currently experimental, and we’re committed to improving it with your valuable feedback.Can you talk about a data science project you've worked on that involved machine learning? Specifically, describe the problem you were trying to solve, the steps you took to approach the problem, and the outcome of the project.
When discussing a project, start by clearly defining the problem you aimed to solve, such as predicting customer behavior or optimizing a supply chain. Explain the data you collected, the methods used for analysis, and the machine learning algorithms you implemented. Highlight any challenges faced during the project and how you overcame them, such as data quality issues or algorithm performance. Finally, conclude with the impact of your project, like increased efficiency or improved decision-making, and any key learnings that might be beneficial for future projects.
How would you explain the concepts of precision and recall to a non-technical stakeholder? Why are these metrics important in evaluating model performance?
To explain precision and recall, start by defining them in simple terms. Precision indicates how many of the predicted positive cases were actually positive, while recall measures how many actual positive cases were identified by the model. Use a relatable analogy, such as a doctor diagnosing a disease, to illustrate the importance of both metrics. Emphasize that in scenarios where false positives or false negatives have significant consequences, understanding these metrics helps in making informed decisions about model deployment and adjustments.
Can you give an example of a time when you encountered data quality issues in a project? How did you identify the problem, and what steps did you take to resolve it?
When discussing data quality issues, start by describing the specific problem you faced, such as missing values, outliers, or inconsistencies. Explain how you identified the issue through exploratory data analysis or data validation techniques. Discuss the steps you took to address the problem, such as cleaning the data, employing imputation techniques, or adjusting your analysis strategy. Conclude with the results of your actions and any insights gained about the importance of data quality in your work.
Cognizant Technology Solutions Data Scientist Interview Process
Typically, interviews at Cognizant Technology Solutions vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.
We've gathered this data from parsing thousands of interview experiences sourced from members.
Cognizant Technology Solutions Data Scientist Interview Questions
Practice for the Cognizant Technology Solutions Data Scientist interview with these recently asked interview questions.