Blend360 Data Scientist Interview Questions + Guide in 2024

Blend360 is a premier AI services provider committed to creating meaningful impact for its clients through data science, AI, technology, and people. The company focuses on leveraging world-class people and data-driven strategies to tackle significant challenges, fostering innovation, and unlocking value for its clients. Blend360 is known for its exceptional people, get-it-done mentality, and delivering high-impact, sustainable results.

In this guide, we’ll tackle how they conduct their data science interviews, along with commonly asked Blend360 data scientist interview questions to help you prepare better. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on a Blend360 data scientist interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Blend360 Talent Acquisition Team will contact you to verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Blend360 Data Scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions. The whole recruiter call should take about 30 minutes.

Initial Online Assessment

The initial online assessment for Blend360’s Data Scientist role consists of coding challenges and database-related questions. The assessment covers:

• 2 questions that can be solved using Python or R.
• 2 SQL questions.

The questions are generally moderately difficult, and you can solve the programming problems using R or Python.

Technical Virtual Interview

After completing the online assessment, the next step is a technical virtual interview. This round typically lasts about 1 hour and involves a deeper dive into the technical skills and knowledge mentioned in your resume. The questions may cover a variety of topics, including:

• Python and SQL coding questions
• Machine Learning fundamentals (e.g., Random Forest, Decision Trees)
• Statistical inference and data analysis
• Case studies on hypothetical business scenarios

Onsite Interview Rounds

You will be invited to the onsite interview loop if you successfully navigate the virtual technical interview. This loop includes multiple rounds conducted with various Blend360 Data Science team interviewers. These rounds will involve:

• Detailed technical discussions focused on your previous project contributions.
• Advanced SQL, Python, and other related technical questions.
• A project presentation in which you will discuss a take-home assignment, presenting your approach, findings, and recommendations.
• Behavioral interviews to assess cultural fit and leadership skills.

Typically, interviews at Blend360 vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

1. Write a SQL query to select the 2nd highest salary in the engineering department.

Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

2. Write a function to merge two sorted lists into one sorted list.

Given two sorted lists, write a function to merge them into one sorted list. Bonus: What’s the time complexity?

3. Write a function missing_number to find the missing number in an array.

You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

4. Write a function precision_recall to calculate precision and recall metrics from a 2-D matrix.

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

5. Write a function to search for a target value in a rotated sorted array.

Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1. Bonus: Your algorithm’s runtime complexity should be in the order of (O(\log n)).

6. How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment?

You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice for this problem?

7. How does random forest generate the forest, and why use it over logistic regression?

Explain how a random forest algorithm generates its forest. Additionally, why might you choose random forest over logistic regression for certain problems?

8. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms. In which scenarios would you prefer a bagging algorithm over a boosting algorithm? Provide examples of the tradeoffs between the two.

9. How would you justify using a neural network model and explain its predictions to non-technical stakeholders?

If your manager asks you to build a neural network model to solve a business problem, how would you justify the complexity and explain the model’s predictions to non-technical stakeholders?

10. What metrics would you use to track the accuracy and validity of a spam classifier for emails?

Assume you have built a V1 of a spam classifier for emails. What metrics would you use to evaluate its accuracy and validity?

11. How would you explain the impact of financial rewards on users’ response rates and improve the experimental design?

You designed an experiment to measure the impact of financial rewards on users’ response rates. The treatment group with $10 rewards had a 30% response rate, while the control group without rewards had a 50% response rate. Explain the results and suggest improvements for the experimental design.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Blend360 data scientist interview include:

• Be Thorough in Your Preparation: Blend360 interviews are rigorous and cover a wide range of topics from SQL and Python to machine learning and case studies. Make sure to practice these skills in depth.
• Showcase Your Soft Skills: This role requires excellent communication skills as well as the ability to translate complex data science concepts into business insights. Prepare to discuss your problem-solving approach and how you’ve effectively communicated technical information in past roles.
• Research the Company: Understanding Blend360’s business model, client base, and industry challenges will help you contextualize your technical answers during case studies and project discussions.

What is the average salary for a Data Scientist at Blend360?

According to Glassdoor, Data Scientists at Blend360 earn between $112K to $164K per year with an average of $138K per year.

What skills are required for a Data Scientist position at Blend360?

To be considered for a Data Scientist position at Blend360, you should have a strong background in programming (Python or R), SQL, machine learning algorithms, and statistical analysis. Additionally, you should possess good communication skills, problem-solving abilities, and the ability to work both independently and as part of a team.

What is the company culture like at Blend360?

Blend360 prides itself on its professional and inclusive environment. The company is known for its commitment to innovation and high standards, and it values the development and well-being of its employees. Collaboration and continuous learning are core components of the company culture.

If you’re ready to tackle challenging problems and deliver impactful results using cutting-edge data science and AI methods, Blend360 is the place for you. Their interview process may be demanding, but it ensures that they hire the best talent to maintain their standard of excellence. As a Data Scientist at Blend360, you’ll work on various projects with a robust support system and ample opportunities for personal and professional growth.

For more insights about the company, check out our main Blend360 Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as data engineer and machine learning engineer, where you can learn more about Blend360’s interview process for different positions.

Good luck with your interview!