Top 22 IBM Data Scientist Interview Questions + Guide in 2024

IBM is one of the most influential technology companies in the world, offering services in various domains such as AI, cloud computing, data analytics, blockchain, and quantum computing.

As they continue their journey to become a more innovative and focused company with the invention of the hybrid cloud, IBM is searching for data science talent year-round. If you’re preparing for a data science interview at IBM, you’re in the right place.

In this interview guide, we’ll walk you through the interview process with our selected IBM data scientist interview questions, strategies, and interview tips. Without further ado, let’s dive in!

Since IBM is a multinational company, the interview processes vary in duration and format. In general, it consists of multiple stages led by different teams with distinct objectives.

Step 1: Initial Screening

If your past experiences meet the job criteria, you’ll be invited to a call with one of the hiring teams. At this stage, the questions will revolve around your application, and none will be technical.

They might ask about your motivation for applying and your knowledge about IBM in general. Therefore, be sure to research the business aspects of IBM before the interview process.

Step 2: Coding Assessments

The data scientist’s role is technical. Therefore, after completing the screening process, you’ll be invited to a coding assessment and asked to complete a multiple-choice challenge to test your programming skills.

In some cases, you might also be asked to complete a live-coding challenge, where you’ll solve an algorithmic question in front of one of the hiring teams. This round typically lasts 30 to 60 minutes.

Step 3: Technical and Behavioral Assessments

This round consists of more than one interview and will be conducted by different team members. The questions will cover both technical and behavioral aspects. Since you’re applying for a data science role, expect technical questions about statistics, probability, and other general data science concepts. For the behavioral part, expect questions to test your interpersonal, problem-solving, and time-management skills.

This round is your chance to show IBM how much you want to work for them. They also recommend that you come prepared with questions to ask them at the end of the interview, as it’s also your opportunity to interview them.

The questions you’ll encounter in a data scientist interview can be divided into technical and behavioral.

Technical questions will test your knowledge of various concepts necessary for success in this role, such as statistics, probability, programming, and SQL. Behavioral questions will assess your motivation and interpersonal skills.

1. Why would you like to work as a data scientist at IBM?

The interviewer wants to assess your motivation for choosing a career in data science and see if it aligns with IBM’s goals, values, and culture.

How to Answer

Before the interview, research IBM’s mission, values, and recent initiatives in data science and technology. Then, during the interview, mention your interest in IBM’s innovative projects and highlight how your skills align with IBM’s vision.

Example

“I am excited about the opportunity to work as a data scientist at IBM because of the company’s groundbreaking work in artificial intelligence, cloud computing, and data analytics. I am particularly drawn to IBM’s focus on developing scalable solutions that have a real-world impact across various industries.

My background in data science, combined with IBM’s resources and collaborative work environment, will allow me to significantly contribute to the company’s mission of advancing technology and driving innovation forward.”

2. What is your approach to resolving conflict with co-workers or external stakeholders, partially when you don’t really like them?

Conflict resolution skills are crucial for effective teamwork and collaboration in any workplace, especially while on a cross-functional team at IBM. This question helps assess your ability to navigate interpersonal challenges professionally and maintain positive working relationships.

How to Answer

Explain your approach to conflict resolution, emphasizing your empathy and professionalism. Discuss your communication skills, problem-solving abilities, and willingness to cooperate to find mutually beneficial solutions.

Example

“My approach to resolving conflicts with co-workers or external stakeholders, even when I may not particularly like them, revolves around open communication, active listening, and empathy. For example, in a previous project, I disagreed with a team member over the allocation of resources. Despite our differences, I began a candid conversation, listened to their concerns, and proposed alternative approaches that addressed both our needs. In the end, we reached a compromise that allowed us to move forward productively and achieve our project objectives.”

3. What was a difficult challenge you overcame in your data science career?

Data science projects are challenging and sometimes fail. Therefore, if you’d like to become a data scientist at IBM, you must have problem-solving skills, resilience, and the ability to overcome obstacles.

How to Answer

Choose a challenge you faced in your data science career and describe how you approached and resolved it. Emphasize your problem-solving skills and ability to learn from failures or setbacks.

Example

“One difficulty I experienced in my data science career was developing a predictive model for customer churn in a highly competitive industry. The dataset was large and complex, with a high degree of imbalance between churn and non-churn instances. Additionally, several missing values and outliers had to be addressed.

To overcome this challenge, I first conducted extensive exploratory data analysis to gain insights into the underlying patterns and relationships in the data. I then implemented various preprocessing techniques, such as data imputation, outlier detection, and resampling methods, to address the imbalance and improve the quality of the dataset.

Next, I experimented with machine learning algorithms and hyperparameter tuning techniques to build and optimize the predictive model. Even with some initial setbacks and challenges with model performance, I persisted in fine-tuning the model and evaluating its performance using rigorous validation techniques.”

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

By asking this question, the interviewer at IBM wants to evaluate your skills, work ethic, and potential for success in your prospective role. Completing projects by going above and beyond the requirements is a desirable trait in a data scientist.

How to Answer

Choose a past project for which you went above and beyond. Outline the situation, your actions, and the results achieved.

Example

“In a previous project, I was tasked with optimizing a machine learning model’s performance to improve customer churn prediction. Recognizing the significance of this task, I conducted extensive research on state-of-the-art techniques and identified areas for improvement. I implemented advanced feature engineering methods, experimented with various algorithms, and fine-tuned hyperparameters to enhance the model’s predictive accuracy.

Additionally, I worked closely with the data engineering team to streamline data preprocessing pipelines, reducing processing time significantly. As a result of these efforts, the updated model achieved a 10% increase in prediction accuracy compared to the previous version, leading to more precise identification of at-risk customers and enabling proactive retention strategies.”

5. What makes you passionate about data science?

IBM seeks data scientists who are genuinely passionate about their field. This devotion often is reflected in their creativity, innovation, and commitment to excellence.

How to Answer

Explain the reasons behind your passion for data science. Include aspects of data science that excite you and any experiences that have fueled your interest. Highlight how your passion will benefit IBM.

Example

“I am passionate about data science because it combines my interest in mathematics, statistics, and technology to solve real-world problems. I am fascinated by the power of data to uncover hidden patterns that can inform decision-making across various industries.

My passion for data science has been further fueled by my experience of developing a machine learning model to predict customer churn during my internship. I am excited about the opportunity to continue learning, growing, and making a difference through data science at IBM.”

6. Given a list of integers, how can you write a function gcd to find the greatest common denominator between them?

If you’d like to apply as a data scientist at IBM, you need to have Python programming skills. This question checks your problem-solving skills and ability to write efficient code.

How to Answer

Implement an efficient algorithm for finding the GCD, such as Euclid’s algorithm. Also, don’t forget to handle edge cases, such as empty lists or lists with a single element.

Example

“First, we’ll handle edge cases: if the list is empty, we’ll return None, and if it contains only one element, we’ll return that element. Then, we’ll define a helper function find_gcd based on Euclid’s algorithm to find the GCD of two numbers. Within the main function, we’ll initialize the GCD variable with the first element of the list and iterate through the remaining elements, updating the GCD using the helper function. Finally, we’ll return the computed GCD.”

def gcd(int_list):
    if not int_list:
        return None
    if len(int_list) == 1:
        return int_list[0]
    
    def find_gcd(a, b):
        while b:
            a, b = b, a % b
        return a
    
    result = int_list[0]
    for i in range(1, len(int_list)):
        result = find_gcd(result, int_list[i])
    return result

int_list = [8, 16, 24]
print(gcd(int_list))  # Output: 8

7. Can you walk us through your approach to feature selection?

At IBM, data scientists work with large datasets to train machine learning models. Therefore, you need to be able to select relevant features to improve model performance and reduce computational overhead.

How to Answer

Mention the techniques you use for feature selection and considerations for dealing with high-dimensional data. Also, demonstrate your understanding of feature importance, dimensionality reduction methods, and the impact of feature selection on model performance.

Example

“My approach to feature selection involves several steps. First, I explore the dataset and understand the relationships between features and the target variable. Then, I use techniques such as correlation analysis and domain knowledge to identify potentially relevant features.

Next, I apply dimensionality reduction techniques such as principal component analysis (PCA) or feature importance estimation methods like tree-based algorithms to reduce the dimensionality of the dataset while preserving as much relevant information as possible.

Then, I evaluate the model’s performance using the selected features and iteratively refine the feature set based on performance metrics such as accuracy, precision, recall, or F1-score.”

8. How can you write a function compute_deviation that takes in a list of dictionaries with a key and list of integers and returns a dictionary with the standard deviation of each list?

One task you’ll have as a data scientist at IBM is writing the code for a particular statistical concept. With this question, your ability to implement statistical computations without relying on external libraries like NumPy will be tested.

How to Answer

Demonstrate your understanding of the standard deviation formula and provide a function that computes the standard deviation for each list of integers in the input list of dictionaries.

Example

”To compute the standard deviation of each list of integers in the input list of dictionaries, we’ll define a function named compute_deviation.

First, we’ll iterate through each dictionary in the input list, extracting the list of values and computing the mean. Then, we’ll calculate the sum of squared differences from the mean, divide by the number of elements minus one, and take the square root to obtain the standard deviation. We’ll store the results in a dictionary with the corresponding keys and return it. ”

def compute_deviation(input):
    output = {}
    for item in input:
        key = item['key']
        values = item['values']
        mean = sum(values) / len(values)
        squared_diff_sum = sum((x - mean) ** 2 for x in values)
        variance = squared_diff_sum / (len(values) - 1)
        std_deviation = variance ** 0.5
        output[key] = round(std_deviation, 2)
    return output

9. Can you explain a complex statistical concept in layman’s terms?

As a data scientist, you need to be able to explain technical concepts clearly. This skill is particularly important at IBM, where you’ll work on a cross-functional team.

How to Answer

Choose an example of a statistical concept and explain it using simple language and everyday examples anyone can understand. Avoid technical jargon and focus on making the concept relatable and easy to grasp.

Example

“Let’s take the concept of p-values, commonly used in hypothesis testing. Imagine you’re a detective trying to solve a crime. The p-value is like the strength of your evidence against a suspect.

If the p-value is low (say, less than 0.05), it means the evidence (or data) you have is quite rare if the null hypothesis (the suspect is innocent) were true. In other words, it suggests that the evidence is strong enough to consider the suspect guilty.

On the other hand, if the p-value is high (greater than 0.05), it means the evidence you have is not very rare if the null hypothesis were true. It’s like finding common fingerprints at the crime scene that could belong to anyone. In this case, you wouldn’t have enough evidence to convict the suspect.”

10. Given two sorted lists, how can you write a function to merge them into one sorted list?

Data scientists at IBM need to possess some fundamental knowledge about data structures and algorithms. Therefore, your ability to solve algorithmic problems and manipulate data structures efficiently will be tested.

How to Answer Implement an algorithm that efficiently combines the two lists while maintaining the sorted order, and discuss the time complexity of your solution.

Example

”To merge two sorted lists into one sorted list, we can iterate through both lists simultaneously, comparing elements and appending them to a new list in sorted order. We’ll start by initializing two pointers at the beginning of each list and compare the elements pointed to by the pointers. The smaller element is appended to the new list, and the corresponding pointer is incremented. We continue this process until we reach the end of either list. If one list still has remaining elements, we append them to the new list. This approach has a time complexity of O(n + m), where n and m are the lengths of the two input lists.”

def merge_lists(list1, list2):
    merged_list = []
    i, j = 0, 0
    while i < len(list1) and j < len(list2):
        if list1[i] <= list2[j]:
            merged_list.append(list1[i])
            i += 1
        else:
            merged_list.append(list2[j])
            j += 1
    merged_list.extend(list1[i:])
    merged_list.extend(list2[j:])
    return merged_list

list1 = [1, 2, 5]
list2 = [2, 4, 6]
print(merge_lists(list1, list2))  # Output: [1, 2, 2, 4, 5, 6]

11. How do you detect outliers in a dataset?

Detecting outliers is an essential step in data cleaning to ensure the quality of your statistical analysis. As you’ll be working with big data at IBM, you must deal with these outliers in each data science project.

How to Answer

Discuss methods commonly used to detect outliers in a dataset, such as z-score, IQR (interquartile range), and visualization techniques. Don’t forget to mention the importance of domain knowledge when determining whether data points are outliers.

Example

“Detecting outliers in a dataset is essential for ensuring the quality and reliability of the data used for analysis and modeling. One common approach is to use statistical methods such as the z-score or IQR (interquartile range) to identify data points that deviate significantly from the mean or median of the dataset. Another approach is to visualize the data using techniques such as box or scatter plots, which can help identify data points that fall outside the expected range of values.

However, domain knowledge and context are important considerations when determining whether a data point is an outlier, as some outliers may be valid data points representing rare events or anomalies in the data.”

12. What are z- and t-tests, and when should one be used over the other?

Hypothesis testing is a common task you’ll perform as a data scientist at IBM. So, understanding the differences between different statistical tests is crucial to ensure the validity of your statistical analysis.

How to Answer

Discuss the purpose of and differences between z- and t-tests. Explain when each test is typically used and under what circumstances we would choose one over the other.

Example

“The z- and t-tests are both statistical tests used to make inferences about population parameters based on sample data. The z-test is typically used when the sample size is large (usually n > 30) or when the population standard deviation is known. It assesses whether the mean of a sample differs significantly from the population mean.

On the other hand, the t-test is used when the sample size is small (usually n < 30) or when the population standard deviation is unknown. It assesses whether the mean of a sample differs significantly from the population mean when the population standard deviation is estimated from the sample.

The key difference between the two tests lies in the assumptions about the population standard deviation: the z-test assumes that the population standard deviation is known, while the t-test does not make this assumption and instead estimates the population standard deviation from the sample.”

13. What is entropy and information gain?

Decision tree is one of the most popular machine learning algorithms due to its performance and interpretability. Since you’ll most likely use this algorithm at IBM, understanding its inner workings is necessary.

How to Answer

Discuss the relevance of entropy and information gain in decision tree algorithms. Explain how entropy measures the impurity in a dataset and how information gain quantifies the effectiveness of a feature in reducing the entropy of the resulting subsets.

Example

“Entropy is a measure of impurity in a dataset. In the context of decision tree algorithms, entropy is used to quantify the uncertainty associated with the classification of data points in a dataset. Higher entropy indicates higher disorder, while lower entropy indicates more homogeneous subsets.

Information gain, on the other hand, measures the effectiveness of a feature in reducing the entropy of the resulting subsets when making split decisions in a decision tree. Features with higher information gain are preferred as they lead to more effective splits, resulting in decision trees that better classify the data.”

14. A team wants to A/B test multiple different changes through a sign-up funnel. They want to see if changing a button from red to blue and/or from the top of the page to the bottom of the page will increase click-through. How would you set up this test?

Since hypothesis testing is a critical topic in data science, expect some study case questions about it during your data scientist interview at IBM.

How to Answer

Outline the steps in setting up an A/B test to evaluate multiple changes in a sign-up funnel. Discuss key considerations such as defining the hypothesis, selecting appropriate metrics, designing variants, determining sample size and duration, and analyzing the results.

Example

”To set up an A/B test for evaluating multiple changes in a sign-up funnel, first I would select appropriate metrics such as click-through rate. Then, I would randomly assign users to each variant to ensure an unbiased comparison between the different changes. Next, I would calculate the necessary sample size to achieve sufficient statistical power and decide on the duration of the experiment to collect enough data.

Finally, by using statistical methods, I would analyze the collected data and determine if any of the changes significantly impact the click-through rate. For this, I would consider factors like statistical significance and confidence intervals.”

15. What do you understand about stochastic gradient descent?

In your interviews to become a data scientist at IBM, expect some questions about general machine learning concepts. Also, gradient descent is a popular optimization method for regression models, which are commonly applied in data science projects at IBM.

How to Answer

Explain stochastic gradient descent by highlighting its importance in training machine learning models and optimizing the model parameters. If possible, also mention practical considerations such as learning rate scheduling and mini-batch sizes commonly used in SGD.

Example

“Stochastic gradient descent (SGD) is an optimization algorithm commonly used in machine learning for training models and optimizing their parameters. Unlike standard gradient descent, which computes the gradient of the loss function using the entire training dataset, SGD updates the model parameters based on the gradient computed on a randomly selected subset of training data, known as a mini-batch. This makes SGD computationally efficient and allows it to handle large-scale datasets.

SGD iteratively updates the model parameters in the direction of the negative gradient of the loss function, aiming to minimize the loss and improve the model’s performance. By updating the parameters incrementally after each mini-batch, SGD gradually converges to a local minimum of the loss function.

Practical considerations such as learning rate scheduling, momentum, and mini-batch sizes play a crucial role in optimizing the performance of SGD and ensuring convergence to an optimal solution.”

16. What is the difference between covariance and correlation?

This question evaluates your knowledge of data analysis and statistics fundamentals. At IBM, you’re expected to know basic statistical concepts and how to apply them in data analysis tasks.

How to Answer

Discuss the difference between covariance and correlation by emphasizing their differences in scale and interpretation.

Example

“Covariance measures the degree to which two variables change together, with positive values indicating a positive relationship and negative values indicating a negative relationship. On the other hand, correlation standardizes covariance by dividing it by the product of the standard deviations of the two variables. This results in a value between -1 and 1, where 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship.

Unlike covariance, correlation is not influenced by the scale of the variables, making it easier to interpret and compare across different datasets. For example, in analyzing the relationship between temperature and ice cream sales, covariance would show how they change together, while correlation would provide insight into the strength and direction of the relationship, facilitating easier interpretation.”

17. What is the difference between precision and specificity?

This question tests your knowledge of general machine learning concepts. After you develop a machine learning model, you need to know how to assess its performance. Precision and specificity are metrics commonly used to determine a model’s performance in a classification problem.

How to Answer

Explain that precision measures the proportion of true positive predictions among all positive predictions made by the model. In contrast, specificity measures the proportion of true negative predictions among all negative predictions made by the model.

Example

“Precision and specificity are both important evaluation metrics used in classification tasks, but they measure different aspects of model performance. Precision measures the proportion of true positive predictions among all positive predictions made by the model.

On the other hand, specificity measures the proportion of true negative predictions among all negative predictions made by the model. It tells us how many of the predicted negative instances are actually negative.”

18. Let’s say you have to draw two cards from a shuffled deck, one at a time. What’s the probability that the second card is not an ace?

Probability is an essential building block in any data science project, as it provides a reliable estimate and interpretation of an analysis. Therefore, you can expect this kind of question to be asked in a data scientist interview at IBM.

How to Answer

Demonstrate your understanding of conditional probability by mentioning that the probability of drawing a non-ace card on the second draw depends on whether an ace was drawn on the first draw.

Example

“When drawing two cards from a shuffled deck, the probability that the second card is not an ace depends on whether the first card drawn is an ace or not. If the first card drawn is an ace, there are now 51 cards left in the deck, of which 48 are non-aces. Therefore, the probability of drawing a non-ace as the second card, given that the first card drawn was an ace, is ⁴⁸⁄₅₁. On the other hand, if the first card drawn is not an ace—there are 52 cards in the deck, of which 48 are non-aces. Hence, the probability of drawing a non-ace as the second card, given that the first card drawn was not an ace, is ⁴⁸⁄₅₂.

Therefore, the overall probability that the second card is not an ace is the average of these two conditional probabilities, weighted by the probability of each event occurring as the first draw, which is ⁴⁄₅₂ for drawing an ace and ⁴⁸⁄₅₂ for drawing a non-ace. Therefore, the overall probability is (⁴⁄₅₂) * (⁴⁸⁄₅₁) + (⁴⁸⁄₅₂) * (⁴⁸⁄₅₂) ≈ 0.9038.”

19. How do you handle imbalanced data?

Imbalanced datasets are extremely common in any real-world data science project, and there’s no exception to this at IBM. Therefore, data scientists at IBM need to understand the theory behind how to deal with imbalanced data.

How to Answer

Discuss various techniques for handling imbalanced data, such as resampling methods (e.g., oversampling, undersampling), algorithmic approaches (e.g., cost-sensitive learning, ensemble methods), and evaluation metrics tailored for imbalanced datasets (e.g., precision, recall, F1-score).

Example

“Handling imbalanced data is a common challenge in machine learning, and several techniques can be used to address this issue. One approach uses resampling methods such as oversampling the minority class or undersampling the majority class to balance the class distribution in the training dataset. Another approach is to use algorithmic techniques such as cost-sensitive learning, where misclassification costs are adjusted to reflect the imbalance in class frequencies.

Additionally, ensemble methods such as bagging and boosting can be effective in handling imbalanced data by combining multiple classifiers trained on balanced subsets of the data.”

20. Given an employees table, how can you write an SQL query to get the total salary of all employees?

As you’ll be working with big data at IBM, SQL is a must-have skill for data scientists. If you’re good at SQL, you can efficiently retrieve the necessary data for your data analysis project.

How to Answer

Write an SQL query that selects the total salary of all employees from the employees table. Make sure the query is syntactically correct and efficiently retrieves the required information.

Example

“This query selects the sum of the salary column from the employees table and aliases the result as total_salary. It calculates the total salary across all employees in the table.”

SELECT SUM(salary) AS total_salary
FROM employees;

21. Let’s say you have a categorical variable with thousands of distinct values, how would you encode it?

Since encoding categorical variables is an essential task in data science, anticipate questions about it during your data science interview at IBM.

How to Answer

Discuss the strategies for encoding a categorical variable with thousands of distinct values, considering both regression and classification models. Key techniques include clustering categories based on the response variable using a shallow decision tree, target encoding based on mean response or conditional probability, grouping by frequency distribution, and applying advanced methods like the Louvain community detection algorithm.

Example

“For a regression model, I would start by sorting the categories based on the response variable and then use a shallow decision tree to cluster them into meaningful groups. This reduces the number of categories while retaining predictive power. Alternatively, I could target encode by replacing each category with the mean response, transforming the categorical feature into a continuous one.

For a classification model, target encoding could involve calculating the conditional probability of the positive class for each category and replacing the categorical values accordingly. Additionally, by analyzing the frequency distribution, I could group infrequent categories into an ‘other’ bucket to simplify the model. If more precision is needed, the Louvain community detection algorithm can be employed to identify natural groupings within the categories without predefining the number of clusters.”

22. Let’s say we’re given a biased coin that comes up heads 30% of the time when tossed. What is the probability of the coin landing as heads exactly 5 times out of 6 tosses?

Since understanding probability distributions is crucial in data science, you may encounter questions involving the binomial distribution during the interview at IBM.

How to Answer

Explain the process of calculating the probability of a specific outcome in a binomial distribution scenario. Key steps include identifying the probability of success, determining the number of trials, applying the binomial probability formula, and performing the necessary calculations.

Example

“Given a biased coin that lands heads 30% of the time, to find the probability of getting exactly 5 heads out of 6 tosses, I would use the binomial distribution formula. First, I would recognize that the probability of heads (success) is 0.3 and the number of trials is 6. Next, I would calculate the binomial coefficient, which represents the number of ways to choose 5 successes out of 6 trials. Then, I would multiply this by 0.3 raised to the power of 5 and 0.7 raised to the power of 1 to account for the probability of getting exactly 5 heads and 1 tail. Finally, after performing these calculations, the probability of getting exactly 5 heads out of 6 tosses is approximately 0.010206.”

Here are some tips to help you gain that competitive edge during a data scientist interview at IBM.

Study the Company and Role

Do this even before sending out your application. Research what IBM is up to, and work through how a data science career at IBM aligns with your career aspirations. This will enable you to personalize your application documents.

In fact, IBM’s website provides a lot of resources that give a sneak peek of the advancements they’re working on. Check out their dedicated blog to see what they’re up to in data science and AI.

Brush Up on Technical Skills

Since a data scientist has quite technical responsibilities, you’ll receive various specialized questions to test the skills necessary to succeed in this role. Common interview questions will cover topics like Python programming, SQL, probability, and statistics.

To help you brush up in these areas, we offer several learning paths, such as Python, SQL, statistics, and probability. To further practice your problem-solving and algorithmic skills, work on solving the challenges in our question bank.

One more tip: if you feel overwhelmed by the wide variety of topics you need to learn, look at the job description and focus on the specific tools or skills listed.

Do Personal Projects

Commitment to continuous learning is a highly desirable trait of a data scientist. Therefore, consider preparing some personal projects to show your passion and expertise in data science. Also, point out if your personal project is closely related to IBM’s business concerns, as it can serve as an engaging discussion point during your interview.

Doing personal projects will also enhance your problem-solving ability as you need to implement different data science concepts throughout the project. To provide you with ideas on topics for your personal data science project, check out our take-home challenges. There, you can also learn step-by-step how to solve a case study using a notebook.

Practice Communication Skills

Data scientists need to communicate findings and complex concepts in an understandable way to non-technical stakeholders. Therefore, it’s a given that you need to practice your communication skills. One effective way to practice is through a mock interview.

In a mock interview, you’ll be paired with a fellow data enthusiast, with whom you can practice as if you’re in a real interview. We know that finding a peer to do a mock interview with is challenging, so we run a mock interview service on our platform to help you connect with like-minded data enthusiasts.

During the mock interview, consider asking case study and behavioral questions to practice your communication skills. You can also check out this guide for some inspiration regarding questions you can ask.

If you need one-on-one coaching with an expert to improve your communication or data science skills in general, you can check out the coaching service offered on our platform.

Below are frequently asked questions by individuals interested in working as a data scientist at IBM.

What is the average salary for a data scientist role at IBM?

The average base salary for a data scientist at IBM is $111,603. In comparison, the average salary for a data scientist position in the US is $123,019. However, if you have extensive experience, you can expect a salary as high as $167,000 at IBM.

For more insights into the salary range of a data scientist at various companies segmented by experience level and location, check out our comprehensive Data Scientist Salary Guide.

What other companies can I apply for besides NVIDIA’s data scientist role?

If you want to work in a tech company with business concerns similar to IBM’s, consider Oracle, Microsoft, or Amazon. We have guides for each company to help you prepare.

For insights on data-related roles in other tech companies, check out our comprehensive company interview guides.

Does Interview Query have job postings for IBM’s data scientist position?

Yes, we have job postings for data scientist positions at IBM, which you can apply for directly through our job portal. However, if you’re interested in discovering opportunities for data scientist positions at other companies, our job board provides an updated list of available positions.

However, we also suggest you check out IBM’s careers page to explore their most recent openings for data scientists or other data-related roles.

To succeed at acing IBM data scientist interview questions, you need to demonstrate proficiency in two different aspects: technical and behavioral. Common technical interview questions at IBM include statistics, probability, Python programming, and SQL. Meanwhile, behavioral questions revolve around what-if situations that provide IBM insights into your personality, work ethic, and problem-solving skills.

If you’re keen on understanding the interview process for other data-related roles at IBM, we’ve got you covered. Check out our IBM guides for business analyst, data analyst, machine learning engineer, product manager, research scientist, and software engineer interviews.

We hope this article helps you prepare for the data scientist interview at IBM. If you have any questions or would like help, get in touch with us on our platform!