Top 23 Tiger Analytics Data Scientist Interview Questions + Guide in 2024

Tiger Analytics is a global company specializing in analyzing data. It helps clients by turning complex data into valuable insights.

Data Scientists at Tiger Analytics play an important role in making business strategies and decisions.

If you are here, then perhaps you are looking for a job as a Scientist at Tiger Analytics, or maybe you are simply looking for a job as a data scientist. This guide will help you understand the interview process, anticipate Tiger Analytics data scientist interview questions, and prepare answers. So, let’s see what the usual interview process is at Tiger Analytics for a Data Scientist role:

The interview process for a Data Scientist at Tiger Analytics aims to evaluate technical skills and cultural fit. It involves written and interview rounds. Here’s an overview of what to expect:

1. Screening Call (30 mins)

This is a preliminary discussion with the recruiter at Tiger Analytics. It’s a chance for both parties to see if there’s a basic match. The recruiter will check if your skills align with the role and discuss practical matters like salary expectations and preferred work location.

2. Technical Written Test (2 hours)

This is a remote test where you’ll be given a case study or a small coding task. It’s designed to assess the core data science skills you need as a data scientist at Tiger Analytics. You might be asked to analyze a dataset or write code to solve a specific problem.

3. Technical Interview (60 mins)

This is a more in-depth evaluation of your technical knowledge as a Data Scientist at Tiger Analytics. You’ll be asked a mix of theoretical questions about statistics and machine learning, and practical coding exercises. The aim is to assess your problem-solving abilities and how you apply your knowledge.

4. Manager Interview (45 mins)

You’ll meet the Hiring Manager at this stage at Tiger Analytics. You’ll discuss your past projects in detail and demonstrate your technical skills. The manager will also assess how well you’d fit into the team.

5. HR Interview (30 mins)

This is a discussion about your career goals and expectations. The HR Representative at Tiger Analytics will explain the company’s policies and culture. You’ll also discuss practical matters like your expected salary.

6. Final Interview (45 mins)

This is a cross-functional discussion with the senior leadership of Tiger Analytics. It’s a chance for them to assess your communication skills. You might be asked to present a project or discuss handling certain situations.

The overall process may take 2-3 weeks from initial screening to offer. Technical and managerial interviews are scheduled within a week of clearing the screening call. The final interview happens in the following week if you successfully clear the previous rounds. You can expect the HR discussion and offer-related procedures in the last 1-2 weeks.

The questioning round in an interview for the role of Data Scientist in Tiger Analytics is intended to judge any candidate’s technical and soft skills.

This round typically includes programming and behavioral questions. In the programming round, you can expect questions about Python, Data structure, Machine Learning and Statistics, Decision trees, etc.

Here are some of the in-depth questions that you can expect to face in a Tiger Analytics Data Scientist Interview:

1. Tell me about a time you had to present complex technical information to non-technical stakeholders. How did you ensure your presentation was clear and accessible?

The interviewer wants to know that as a Data Scientist at Tiger Analytics, you can present complex analysis to non-technical audiences in a simple, easy-to-understand way.

How to Answer

Explain how you simplified the information, avoided jargon, used analogies/examples, and tailored the presentation to the audience, and how you used storytelling to keep your audience interested.

Example

“When I presented the results of model validation to senior leaders who were not data scientists, I made sure to keep the technical terms to a minimum. Instead, I focused on explaining how the new model would improve important business metrics compared to the previous version. I used a simple framework for my presentation, covering our goals, approach, results, and recommendations. To make the benefits more tangible, I gave real-world examples. After my presentation, several leaders complimented me on how clear and easy to understand it was”.

2. How have you handled situations where business stakeholders made requests without understanding analytics limitations?

As a Data Scientist, when you work at Tiger Analytics, you will come across situations when you have to manage stakeholder expectations tactfully. This question tests your ability for that.

How to Answer

Show that you set realistic expectations from the start based on available data, analytics maturity, etc. If incorrect assumptions occur, highlight how you guide them in the right direction by explaining the limitations or offering alternative solutions. Share examples of how you used soft skills to maintain trust.

Example

“When one of our product managers was disappointed with the performance of our predictive model, I showed him the validation results that met our documented goals. I also explained that the model had limitations due to data quality issues. To address this, I suggested running A/B tests to capture more customer data instead of immediately rebuilding the model. This approach helped the product manager feel heard and also set realistic expectations.“

3. Tell me about a time you uncovered an insight that required changing the direction of a project. How did you convince stakeholders this was the right path?

As a Data Scientist at Tiger Analytics, will you be able to know when analysis requires a new direction and then convince stakeholders to move forward based on that recent insight backed by data? This question aims to discover that.

How to Answer

Share your approach for validating surprising findings and overcoming any stakeholder doubts through storytelling, visualization, testing, etc. Emphasize that you shift directions based solely on insights from the data.

Example

“While analyzing user data, I noticed a significant drop in engagement for a core app feature. We were planning to invest heavily in it. After checking various groups of users, I suggested to our leadership team that we remove the feature based on these findings. To convince them, I created simple visualizations highlighting the downward engagement trends and predicted impact on long-term retention. I also proposed running an A/B test before completely removing the feature to help build consensus. Leadership was convinced by the data, and we switched our focus to other features with better engagement and retention rates.”

4. Can you share an example of a project where your strengths really shone through, and another where your weaknesses were exposed? How did you handle these situations?

When Tiger Analytics interviews data scientist candidates, they ask a question to assess their adaptability and self-awareness. It’s important for candidates to understand their strengths and weaknesses and how to navigate challenges.

How to Answer

Use the STAR method (Situation, Task, Action, Results) to structure your response. For the strengths part, discuss a project where your skills and strengths were pivotal to its success. For the weaknesses part, talk about a project where you faced challenges due to your weaknesses and, more importantly, how you overcame those challenges or what you learned from the experience.

Example

“Strength: At XYZ Corp, I worked on a project to predict customer churn. I used Python and my strong analytical skills to build a model that accurately predicted when customers would leave. This helped the company keep valuable customers and increase revenue.

Weakness: While working on a project at ABC Inc., I discovered that my presentation skills needed improvement. Although I had created a complex machine-learning model, I found it challenging to explain it to non-technical stakeholders. I recognized that this was a weakness and took a course on data storytelling to enhance my presentation skills. Afterward, I was able to explain my models more effectively in subsequent projects, making sure that the insights were understood and actionable.”

5. Walk me through a complex problem you faced recently. How did you approach analyzing and solving it?

When you are giving an interview for the Data Scientist role at Tiger Analytics, and this question is asked, it means the interviewer wants to know how well you can solve complicated problems. The interviewer will look for signs of logical thinking, creativity, perseverance, and the ability to work under pressure. The goal is to assess the candidate’s analytical skills.

How to Answer

Answer this question by first describing the problem in detail, then explaining the step-by-step process you used to analyze and solve it. You should highlight your analytical thinking, creativity, and perseverance throughout the process.

Example

“A systematic approach is the best to deal with any complex problem. In my previous role as a project manager, we were faced with a significant challenge when a team member resigned in the middle of a critical project. He was the only one who had specific knowledge about an essential part of the project. I analyzed the problem and how his departure will impact our project timeline. Based on my analysis, I identified two possible solutions: redistribute the work among existing team members or hire a new team member. I decided to apply a combination of both solutions. I split the workload between the existing team members and hired a temporary consultant to handle the more complex tasks. This approach allowed the team to complete the project without major delays. Through this experience, I learned that in project management, quick decision-making and the ability to adapt are key factors for success.”

6. Can you describe a project where you had to improve the precision of a model, such as a recommendation system, and what was the impact on user engagement?

This question is designed to assess a data scientist candidate’s ability to improve model accuracy and understand how their work impacts user engagement. It helps the interviewer at Tiger Analytics understand how the candidate approaches challenges regarding accuracy.

How to Answer

Discuss a project where you were tasked with improving the precision of a model. Explain the steps you took, the techniques you used, and the impact it had on user engagement or other relevant metrics.

Example

“In my previous job at XYZ Corp, I improved the e-commerce platform’s recommendation system using a collaborative filtering algorithm. I fine-tuned it through A/B testing and cross-validation techniques while incorporating user feedback. These efforts resulted in a 20% increase in user engagement and a 15% increase in sales.”

7. How would you reverse a linked list in place without extra memory?

This tests your understanding of linked lists and your ability to code an efficient algorithm for the Data Scientist role at Tiger Analytics.

How to Answer

Explain your thought process and walk through reversing a linked list iteratively and recursively. Discuss tradeoffs like readability vs performance. Show pseudo-code or actual runnable code examples.

Example

Pseudo-code:

Iterative:

Set previous = null

Set current = head

While current is not null:

Set nextTemp = current.next

current.next = previous

previous = current

current = nextTemp

Set head to previous

8. Imagine you have a collection of lowercase strings, each string composed of letters from ‘a’ to ‘z’. Can you devise a function, named calculate_alphabet_sum, that takes this collection as input and outputs a list, where each element is the sum of the positions of the letters in the corresponding string (considering ‘a’ as 1, ‘b’ as 2, and so on)?

This question tests a data scientist’s problem-solving and coding skills at Tiger Analytics. It helps the interviewer assess their approach, code structure, and efficiency for data analysis tasks.

How to Answer

Explain your approach to the problem, write the code, and then explain what the code does.

Example

def calculate_alphabet_sum(strings):

sums = []

for string in strings:

	sum = 0

	for char in string:

	sum += ord(char) - ord('a') + 1

sums.append(sum)

return sums

This function iterates over each string in the input list. For each string, it calculates the sum of the positions of its letters in the alphabet using the ord function and adds it to a list. The function then returns this list.

For example, calculate_alphabet_sum([‘abc’, ‘def’]) would return [6, 15], because the sum of the positions of the letters in ‘abc’ is 6 and in ‘def’ is 15.”

9. Explain Bayes’ Theorem and how it’s used.

Bayes’ Theorem is fundamental in probabilistic machine learning models. Tiger Analytics, focusing on data-driven solutions, would want to assess your understanding of this theorem.

How to Answer

Define the theorem, explain its components, and describe its application in data science.

Example

“Bayes’ Theorem calculates the probability of an event based on prior knowledge of conditions related to the event. It’s expressed as P(A|B) = [P(B|A) * P(A)] / P(B). In data science, it’s used in Bayesian inference for statistical analysis and in algorithms like Naive Bayes for classification tasks.”

10. Suppose you have two lists, each sorted in ascending order. Can you construct a function, named merge_sorted_lists, that combines these two lists into a single list that is also sorted in ascending order?

This question evaluates the problem-solving and coding skills of data scientist candidates at Tiger Analytics. It helps understand their approach, code structure, and efficiency in data manipulation and analysis tasks.

How to Answer

Explain your approach to the problem, write the code, and then explain what the code does.

Example

def merge_sorted_lists(list1, list2):

return sorted(list1 + list2)

This function simply concatenates the two input lists using the + operator, and then sorts the resulting list using the sorted function.

For example, merge_sorted_lists([1, 3, 5], [2, 4, 6]) would return [1, 2, 3, 4, 5, 6].

11. Explain the differences between parametric and nonparametric statistical tests.

Understanding the difference between parametric and nonparametric tests is crucial for selecting the right statistical test in data analysis, a key skill for a Data Scientist at Tiger Analytics.

How to Answer

Define both terms and explain their differences and when to use each.

Example

“Parametric tests assume underlying statistical distributions and are used when data meets certain conditions like normality. Nonparametric tests make fewer assumptions about the data’s distribution. For example, t-tests (parametric) and Mann-Whitney U tests (nonparametric) are used for comparing two groups.”

12. Can you elucidate the distinctions between Lasso Regression and Ridge Regression?

This question assesses a data scientist candidate’s knowledge of regularization techniques in linear regression, crucial for effective predictive models at Tiger Analytics.

How to Answer

Discuss the key differences between Lasso and Ridge Regression, their advantages and disadvantages, and when to use each technique.

Example

“Ridge Regression and Lasso Regression are two methods to help simplify models in data analysis. Ridge Regression reduces model complexity by shrinking coefficients, but it doesn’t make them zero or select features. Lasso Regression, on the other hand, can result in feature selection by reducing some variable coefficients to zero. If feature selection is important, Lasso Regression might be the better choice. If all features are important, Ridge Regression is a better choice.”

13. What is the difference between Type I and Type II errors?

Understanding Type I and Type II errors is essential for hypothesis testing, a key aspect of a Data Scientist’s role at Tiger Analytics.

How to Answer

Define both errors and explain their implications in statistical testing.

Example

“Type I error (false positive) occurs when we reject a true null hypothesis. Type II error (false negative) happens when we fail to reject a false null hypothesis. For instance, in a drug efficacy test, a Type I error would mean declaring the drug effective when it’s not, and a Type II error would mean declaring it ineffective when it actually works.”

14.  Can you explain the process of transforming categorical features into a format that can be used in machine learning algorithms?

This question assesses a data scientist’s understanding of handling categorical features in ML models. It helps the interviewer evaluate the candidate’s data preparation skills, crucial for effective predictive modeling at Tiger Analytics.

How to Answer

Discuss the concept of encoding categorical features, why it’s necessary, and the different methods available.

Example

“Categorical features are variables that contain labels instead of numbers. Some machine learning algorithms can’t work with label data directly. They need all input and output variables to be numbers. This means that categorical data must be changed into a numerical form. There are different ways to do this, such as Label Encoding, One-Hot Encoding, and Binary Encoding.”

15. What are some common regression diagnostic plots, and what do they indicate?

Diagnostic plots are used to check the assumptions of regression models. For a data scientist role, Tiger Analytics would want to check your ability to interpret these plots.

How to Answer

List some common diagnostic plots and explain what they indicate about the model.

Example

“Residual plots check assumptions like linearity, equal variance, and normality of errors. Q-Q plots also assess normality. Scale-Location plots detect heteroscedasticity. Added variable plots identify influential observations. These help validate and improve the model.”

16. Can you describe how you would design a model to detect fraudulent transactions in a banking system?

This question assesses a data scientist candidate’s ability to build predictive models for fraud detection, which is crucial for maintaining banking system security.

How to Answer

Discuss the steps involved in building a fraud detection model, the challenges, the type of data needed, the choice of algorithms, and how to evaluate the model.

Example

“To create a fraud detection model, you need to gather past transaction data first. Then, you must process this data, handle any missing values and balance it. Once done, build a machine learning model using appropriate algorithms and engineer new features that could help in detecting fraud. Next, evaluate the model using suitable metrics. Finally, deploy the model in the actual system and keep monitoring and updating it regularly.”

17. Explain how K-means clustering works. What are its limitations?

K-means is a popular clustering algorithm. The interviewer at Tiger Analytics would want to assess your understanding of it and its limitations as a Data Scientist.

How to Answer

Explain the algorithm’s steps and its limitations.

Example

“K-means is an unsupervised algorithm that partitions data points into k clusters. It randomly initializes k cluster centers, assigns points to their closest center, recomputes cluster centers as the mean of points, and repeats until converged.”

18. Can you explain the concept of validity in the context of an experiment and why it’s important?

The question aims to evaluate a data scientist’s understanding of experiment design and reliability to draw meaningful conclusions from data at Tiger Analytics.

How to Answer

Discuss the concept of validity, its types, why it’s important, and how to ensure it in an experiment.

Example

“When conducting an experiment, it’s important to measure what you intend to measure accurately. This is called validity, and it affects the reliability of the experiment’s results. To ensure validity, it’s important to carefully design the experiment, control variables that can affect the results, use proper sampling methods, and use appropriate statistical tests. There are four types of validity: internal, external, construct, and conclusion validity. Validity means that your research findings are trustworthy and accurate.”

19. What are the main principles of good data visualization?

Good data visualization aids data scientists in understanding complex data. The interviewer at Tiger Analytics aims to assess your ability to present data effectively.

How to Answer

Discuss the principles that guide effective data visualization.

Example

“Good data visualization should be clear, concise, and intuitive. It should accurately represent the data, highlight the important features without distorting the information, and be designed with the audience in mind. Use of appropriate scales, colors, and labels enhances readability and understanding.”

20. Can you describe how you would design and analyze an A/B test for a change in a button on a website?

A/B testing is a crucial skill for data-driven decision-making. This question aims to assess a data scientist’s understanding of this skill. It helps the interviewer at Tiger Analytics understand how the candidate designs experiments, analyzes results, and makes data-driven recommendations for optimizing user experience and engagement.

How to Answer

A/B testing is a crucial skill for data-driven decision-making. This question aims to assess a data scientist’s understanding of this skill. It helps the interviewer understand how the candidate designs experiments, analyzes results, and makes data-driven recommendations for optimizing user experience and engagement.

Example

“When you want to run an A/B test, you should follow several steps. First, come up with a clear hypothesis. For example, you might think that changing the color of a button from blue to green will increase the number of people who click on it. Next, divide your users into two groups: one group that sees the old button and one group that sees the new button. It’s important to randomly choose which group each user belongs to. After that, choose a metric to measure the effect of the change, such as the number of clicks. Then, run the test for long enough to collect enough data. Once you have enough data, analyze it using the right statistical tests to see if there’s a significant difference between the two groups. Based on the results, decide whether to implement the change, keep running the test for longer, or try a different change.”

21. Can you explain the differences between Z-tests and t-tests, and provide a scenario where each would be appropriately used?

This question assesses a data scientist candidate’s understanding of statistical hypothesis testing and how they choose the appropriate test based on data and the problem at hand for making data-driven decisions at Tiger Analytics.

How to Answer

Discuss the differences between Z-tests and t-tests and their assumptions, and provide examples of when to use each test.

Example

“Z-tests and t-tests are statistical tests to check if two groups have different average values. A Z-test is used with a large sample size (generally greater than 30) and known population variance. A t-test is used with small sample sizes (generally, less than 30) and unknown population variance.”

22. How would you create an application that allows anyone to review restaurants?

Designing a database schema is a fundamental skill for creating a robust application. This question aims to assess a candidate’s understanding of data modeling and database design principles. It helps the interviewer understand how the candidate organizes data, defines relationships between entities, and ensures data integrity within the application.

How to Answer

When designing a database schema, consider the core entities and their relationships. In this case, the main entities are users, restaurants, and reviews. Each table should be well-defined with appropriate columns, data types, and constraints to maintain data integrity and support application functions.

Example

“When designing a database schema for a restaurant review app, I focus on the core entities: users, restaurants, and reviews. The users table to store user profiles include columns like id for unique identification, created_at for the signup timestamp, updated_at for profile updates, name for the user’s name, and picture for profile picture links. The restaurants table to store restaurant information include columns like id for unique identification, created_at for the creation timestamp, updated_at for updates, name for the restaurant’s name, and images for links to restaurant images. The reviews table to store user reviews include columns like id for unique identification, created_at for the review creation timestamp, updated_at for updates, restaurant_id for the reviewed restaurant, user_id for the review author, picture for review images, rating for the user’s rating, and comment for the review text. This structure ensures data integrity and supports the app’s main functions.”

23. Write a function, digit_accumulator, that returns the sum of every digit in the string.

This question evaluates the problem-solving and coding skills of data scientist candidates. It helps understand their approach, code structure, and efficiency in handling string manipulation and numerical calculations.

How to Answer

Explain your approach to the problem, write the code, and then explain what the code does.

Example

def digit_accumulator(s):
    accumulator = 0
    for char in s:
        if char in '0123456789':
            accumulator += int(char)
    return accumulator

This function iterates through each character in the string, checks if it is a digit, and if so, converts it to an integer and adds it to the accumulator. For example, digit_accumulator(“123”) would return 5, as the sum of the digits 1, 2, 3 is 5.

Understand the Company’s Culture

Before the interview, research Tiger Analytics’ mission, values, and culture. Understanding the company’s culture can help you tailor your responses to align with their values.

You should also check out Interview Query’s data scientist learning resources to find out more about the areas typically covered by data scientist interview questions.

Be Clear on Basics

Make sure you have a strong understanding of basic concepts in data science, statistics, and machine learning. You should be able to explain these concepts clearly and concisely. You can take the help of many interview questions tailored for this position on Interview Query.

Know Your Resume

Be prepared to discuss any data science projects, experiences, or skills listed on your resume. You should be able to explain what you did, how you did it, and what the results were.

Practice Problem-Solving

Data science is all about problem-solving. Practice solving problems related to your field, and be ready to explain your thought process during the interview. Interview Query’s coaching service can help you to get better at this. You can get professional guidance to help you answer interview questions correctly.

Showcase Your Communication Skills

As a Data Scientist, you’ll need to communicate complex ideas to non-technical stakeholders. Demonstrate your ability to explain technical concepts in simple terms.

Ask Insightful Questions

Asking questions shows your interest in the role and the company. Prepare some thoughtful questions about the role, the team, or the company.

Stay Current

Data science is a rapidly evolving field. Show that you’re up-to-date with the latest trends and technologies by discussing recent developments or projects you’ve worked on. Read our blog to stay updated with the industry trends.

Tiger Analytics is a data science consultancy for Fortune 500 companies. This FAQ has quick answers for those interested in joining as a Data Scientist.

How Much Do Data Scientists at Tiger Analytics Make In A Year?

Based on our estimates of various data points, the estimated average base salary for a Data Scientist at Tiger Analytics is US$123,687 per year.

Check out the average base salary and the average total compensation for data scientists in general on Interview Query’s Data Scientist Salary page.

Is there any resource available on Interview Query where I can find information about the interview experiences of other candidates who applied for Tiger Analytics’ data scientist position?

If you’re looking for interview experiences for data scientist roles at Tiger Analytics, you won’t find them on Interview Query. However, you can check our interview experiences section to find other people’s interview experiences at other companies for different roles.

As you prepare for your Tiger Analytics data scientist interview questions, approach them with confidence and a positive mindset. For more information, go to our main Tiger Analytics Interview Guide. We have also covered their Data Analyst and Data Engineer roles, so those will provide you with additional information.

A Data Scientist position at Tiger Analytics is highly sought after due to the company’s reputation for innovation and excellence. However, this role is also challenging and rewarding. Whenever you feel like you need more help preparing for this role, you can always check our database of data scientist interview questions, which include behavioral questions as well as project interview questions, and helpful case study interview questions.

We believe that preparation is the key to success and are committed to providing you with the tools you need to shine.

If you need further help, please don’t hesitate to contact us. Our services are designed to provide detailed support, covering all aspects of the interview process.

We are here to help you succeed and encourage you to make the most of our resources.