Top 22 GEICO Data Scientist Interview Questions + Guide in 2024

As a leading insurance provider in the US, GEICO serves millions of policyholders, offering them peace of mind with its comprehensive coverage and innovative insurance solutions. With more than 16 million auto policies in play, Geico strives to keep improving the satisfaction of its customers through personalized insurance solutions, from vehicle to business insurance.

To achieve this mission, GEICO constantly seeks data science talents who can extract meaningful information from their huge volumes of data.

If you’re gearing up for a GEICO data scientist interview, you’re in the right spot. This guide offers several commonly asked interview questions tailored to the position, complete with an example of how to answer each question. So, without further ado, let’s dive in!

Like many technical roles, the interview process for a data scientist position at GEICO may vary in duration and format. However, it consists of multiple stages, each led by different teams with distinct objectives.

Application Screening and Recruiter Calls

Your interview journey at GEICO starts with the recruitment team evaluating your application documents, which typically include your cover letter and resume. During this phase, the hiring team evaluates whether your qualifications and skills align with the job criteria.

If your qualifications meet the job requirements, you’ll be invited to a call with one of the recruiters. In this stage, the recruiter will ask about items in your resume or cover letter and your career motivation and goals.

First Technical Round

If the call with the recruiter went well, you’ll progress to the first technical round. This round involves some coding questions, and you need to demonstrate your skills in programming languages like Python and SQL. The questions themselves resemble those found on platforms like Interview Query or LeetCode. In this stage, the interviewer wants to test your algorithmic programming skills and how well you turn technical concepts into code.

Second Technical Round

During the second technical round, you will be given with a case study related to machine learning concepts to gauge your machine learning knowledge and problem-solving abilities. You need to answer the question correctly and show your ability to articulate your thought process when addressing a specific use case.

In some areas, instead of an on-site interview, you’ll be given a use case in the form of a take-home challenge. This means they’ll give you a machine learning task related to problems commonly found in the insurance domain. Then, you will have time to solve it before you present your solutions to them.

Third Technical Round

The third technical round is the final interview for a data scientist position at GEICO. In this stage, you’ll encounter behavioral questions to evaluate your interpersonal and communication skills. Additionally, you’ll have the opportunity to discuss your career aspirations and what you anticipate from your potential role at GEICO.

The questions you may face in a data scientist interview cover a range of topics, from technical to behavioral. In this section, we’ll dive into typical technical and behavioral questions you’ll find in a GEICO data scientist interview.

1. What drives your passion for data science?

Any company, including GEICO, wants a data scientist with genuine motivation, enthusiasm, and commitment to improving their skills. This question checks your motivation for data science and how you can drive their business forward with your passion.

How to Answer

You should begin by reflecting on personal experiences, interests, or events that sparked your passion for data science. Discuss the aspects of data science that excite you the most, such as solving complex problems, extracting meaningful insights from data, and making a significant impact through data-driven decision-making. Explain how your passion drives you to continuously learn, adapt, and innovate in the rapidly evolving field of data science.

Example

“My passion for data science started during my undergraduate studies when I worked on a research project to analyze and predict stock market trends using machine learning algorithms. I was fascinated by the potential of data science to uncover hidden patterns and make accurate predictions that influence real-world financial decisions. This experience motivated me to pursue further studies and career opportunities in data science.

Since the field of data science is rapidly evolving, I’m committed to constantly learning and adapting to new technologies and methodologies to stay current with its advancements.”

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

A reliable data scientist who exceeds expectations is wanted in any company, and GEICO is no exception. Surpassing expectations in a project shows your commitment, dedication, and capability to deliver high-quality work, which are essential attributes for success and innovation within any organization.

How to Answer

Draw from your personal experience. Discuss the project’s challenges or goals, the actions you took, and the strategies you employed to exceed the expected outcomes. Explain how you demonstrated initiative, creativity, and determination to overcome obstacles and deliver exceptional results. Also, highlight the impact of your contributions on the project’s success and the recognition or feedback you received for exceeding expectations.

Example

“During a data analytics project to improve customer segmentation in my previous company, I noticed that the existing segmentation methods were not effectively capturing our customer base’s diverse needs and preferences. Seeing the opportunity to enhance the project’s impact, I took the initiative to research and implement advanced machine learning techniques and algorithms to develop a more sophisticated and accurate customer segmentation model.

I collaborated with cross-functional teams to integrate additional data sources and refined the segmentation criteria to create more targeted and personalized customer segments. As a result of my contributions and efforts, the new segmentation model significantly improved the accuracy of customer targeting and contributed to a 20% increase in customer engagement and satisfaction.”

3. When working on complex data projects, how do you motivate your team members and offer support to guide them to project completion?

This question evaluates your interpersonal skills, such as leadership, teamwork, and communication skills. Data scientists in a big company like GEICO are constantly collaborating in a team. So, the ability to support, guide, and encourage team members is essential for fostering a positive and productive work environment, ensuring project completion, and achieving the company’s goals.

How to Answer

Start by discussing your strategies to motivate and support team members, such as setting clear goals, providing constructive feedback, and recognizing and celebrating achievements. Then, explain how you foster a collaborative and supportive work environment by encouraging open communication, sharing knowledge and resources, and promoting a culture of continuous learning and growth.

Example

“Let me explain my approach by sharing a personal experience. During a previous data project, my team faced challenges with data quality and the complexity of the analysis, which led to some members feeling overwhelmed. To address this, I organized a team meeting to discuss the challenges and concerns and to collaboratively develop a plan to overcome the obstacles. I set clear and achievable goals for each team member, provided constructive feedback, and offered additional support and resources to those who needed it.

I also recognized and celebrated small victories and milestones to keep the team motivated and focused on project completion. By fostering a collaborative and supportive work environment, encouraging open communication, sharing knowledge and resources, and promoting a culture of continuous learning and growth, I was able to motivate and guide my team members to successfully complete the project.”

4. How do you prioritize tasks and stay organized when you have multiple deadlines?

This question evaluates your organizational and time management skills and ability to prioritize tasks effectively and manage multiple deadlines efficiently. Data scientists at big companies like GEICO often work on multiple projects simultaneously. They must be able to prioritize tasks, allocate time and resources effectively, and meet deadlines to ensure the timely completion of projects and the delivery of high-quality work.

How to Answer

Discuss the strategies you use to prioritize multiple deadlines, such as assessing the urgency and importance of each task, setting clear and realistic goals and deadlines, and allocating time and resources efficiently. Then, explain how you stay organized using tools and techniques to manage tasks, track progress, and maintain clear communication with stakeholders and team members. Share a personal example of when you successfully managed multiple deadlines by applying these strategies and staying organized to ensure you completed the projects on time and delivered high-quality work.

Example

“When faced with multiple deadlines, I prioritize tasks by considering the urgency and importance of each project, setting clear and realistic goals and deadlines, and allocating time and resources efficiently to ensure the projects are completed on time and my work is high-quality. To stay organized, I use project management tools and techniques, such as Gantt charts and to-do lists.

For example, during a previous data analytics project, I had to manage multiple tasks and deadlines simultaneously. I prioritized the tasks based on their importance and deadline, created a detailed project plan with clear milestones and deadlines, and regularly communicated with my team to track progress and address any challenges or issues that arose. As a result of my organizational and time management skills, adaptability, and effective communication, I successfully managed multiple deadlines and delivered the project on time.”

5. Describe a situation where your initial analysis did not yield the expected results. How did you troubleshoot the issue and refine your approach?

It’s common in a data science project that the analysis result doesn’t yield the expected results. Therefore, during the interview at GEICO, you need to demonstrate your problem-solving skills, adaptability, and ability to critically evaluate and refine your analytical approaches in such cases.

How to Answer

Share a specific situation where your initial analysis did not yield the expected results. Discuss the steps you took to troubleshoot the issue, such as reviewing data quality, exploring alternative analytical methods, and consulting with team members or experts. Explain how you critically evaluated the results, identified potential issues or errors in the first approach, and refined your analytical approach to address the issues and improve the accuracy and reliability of the results.

Example

“During a project to analyze customer behavior and preferences in my previous company, my initial analysis did not yield the expected results—the patterns and insights were inconsistent with our hypotheses and expectations. To troubleshoot the issue, I reviewed the data quality, looked at alternative analytical methods, and talked with my team members to hear their thoughts.

After evaluating the results, I found potential issues and errors in the initial approach, such as data preprocessing and feature selection. I then refined my analytical approach by improving the data preprocessing steps and adjusting the feature selection criteria.”

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

To become a data scientist at GEICO, you need to demonstrate your proficiency in coding. This question checks your coding, problem-solving skills, and understanding of data manipulation.

How to Answer

First, explain the approach you would take to merge two sorted lists into one, such as iterating through the lists and comparing the elements to determine the order in which they should be merged. Then, discuss the steps of the algorithm and the solution’s time and space complexity.

Example

“To merge two sorted lists into one sorted list, I would use a simple iterative approach where I iterate through both lists and compare the elements to determine the order in which they should be merged. The time complexity of this solution is O(n + m), where n and m are the lengths of the two lists, and the space complexity is O(n + m) to store the merged list. Below is a Python code implementation of the function to merge two sorted lists:”

def merge_sorted_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
 
 while i < len(list1):
	 merged_list.append(list1[i])
	 i += 1
 
 while j < len(list2):
	 merged_list.append(list2[j])
	 j += 1
 
 return merged_list

list1 = [1, 3, 5]
list2 = [2, 4, 6]
merged_list = merge_sorted_lists(list1, list2)
print(merged_list) # Output: [1, 2, 3, 4, 5, 6]

7. How do you explain the prediction of a decision tree model?

As an insurance company, GEICO often implements machine learning models in which the prediction can be easily explained. So, it is important to understand the inner workings of algorithms like linear regression or decision trees and how they come up with the prediction.

How to Answer

Explain a decision tree algorithm and its use case. Then, discuss how decision tree models make predictions. This can include examining how the model splits the data based on feature values to create a tree-like structure and how it uses this structure to classify new data points.

Example

“A decision tree model makes predictions by recursively splitting the feature space into subsets based on the most informative features. At each node of the tree, the model selects the feature that best separates the data into different classes. This process continues until the data is completely classified or a stopping criterion is met.

To predict the class of a new data point, we traverse the tree from the root node down to a leaf node, following the splits based on the feature values of the new data point. The class associated with the leaf node reached by the data point is then assigned as the prediction.”

8. Let’s say we have a table with an ID and name fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database. How can you write a query to randomly sample a row from this table?

SQL is an essential skill for a data scientist, especially at GEICO, where you’ll deal with large volumes of data. This question assesses your SQL querying skills, understanding of database optimization, and ability to handle large datasets efficiently.

How to Answer

Begin by explaining the SQL query you would use to sample a random row from the table, such as using the ORDER BY RANDOM() clause in the SQL query. Next, discuss the steps of the SQL query and the potential issues with performance and database throttling. Provide a clear and concise SQL query implementation to sample a random row from the table efficiently without impacting the database performance.

Example

“To sample a random row from the big_table without throttling the database, I would use the following SQL query:

SELECT * FROM big_table
ORDER BY RANDOM()
LIMIT 1;

This SQL query will randomly order the rows in the table and select the first row, effectively sampling a random row from the table. The ORDER BY RANDOM() clause may not be the most efficient method for large tables as it can be computationally expensive and may impact the database performance.“

9. What is the difference between bagging and boosting?

The ensemble method is quite common in an insurance company like GEICO. It is used to improve the predictive power of interpretable models like linear regression or decision trees. This question assesses your understanding of the different techniques of the ensemble method and their concepts.

How to Answer

Start by describing bagging and boosting methods and where we can normally implement them. Then, explain the core differences between bagging and boosting, focusing on the underlying techniques and how they handle the training data and model ensemble creation.

Example

“Both bagging and boosting are ensemble learning techniques that combine multiple machine learning models to improve predictive performance. However, they differ in their approach:

Bagging (Bootstrap Aggregating):

• Technique: Bootstrapped samples of the training data are used to train multiple independent models in parallel.
• Model Training: Each model is trained on a random subset of the data with replacement.
• Model Diversity: The models are diverse as they are trained independently.
• Combination: Predictions are averaged (for regression), or majority voting is used (for classification) to make the final prediction.

Boosting:

• Technique: This technique trains a sequence of weak learners (typically shallow trees or stumps), where each model corrects the errors of its predecessor.
• Model Training: Data points that are misclassified by the previous model are given more weight in the next model’s training.
• Model Diversity: The models are sequential and focus on correcting the mistakes of the previous models.
• Combination: Predictions are combined using a weighted sum of the individual models’ predictions.”

10. How would you interpret coefficients of logistic regression for categorical and boolean variables?

Your understanding of common interpretable machine learning algorithms like logistic regression will be very important at GEICO. Insurance companies prefer applying traditional machine learning that can be easily interpreted to deep neural networks. This question gauges your understanding of the logistic regression concept.

How to Answer

Explain the general interpretation of coefficients in logistic regression. For categorical variables, discuss how to interpret the coefficients in comparison to a reference category. For boolean variables, explain how to interpret the coefficient as the log-odds change associated with a one-unit change in the boolean variable.

Example

“In logistic regression, the coefficients represent the log-odds change in the dependent variable for a one-unit change in the predictor variable, holding other variables constant.

For categorical variables with multiple categories, the coefficient for each category is interpreted relative to a reference category. A positive coefficient for a category indicates an increase in the log-odds of the outcome relative to the reference category, while a negative coefficient indicates a decrease.

For boolean variables, a positive coefficient represents an increase in the log-odds of the outcome when the boolean variable is true compared to when it is false. Conversely, a negative coefficient indicates a decrease in the log-odds of the outcome when the boolean variable is true compared to when it is false.“

11. What techniques or methods do you normally use to manipulate big data?

Data scientists at big companies like GEICO are expected to work with huge volumes of data. This necessitates skill in big data, particularly data preprocessing techniques, efficient data storage solutions, advanced data transformation techniques, and specialized big data processing tools and technologies. Knowledge of these techniques is essential for extracting meaningful insights and building robust predictive models.

How to answer

Describe the techniques you normally use for data preprocessing and transformation. Then, mention the big data processing tools and technologies you use for data streaming, querying, and analysis. Remember to also mention the techniques you usually use to store and transfer data efficiently in the context of big data.

Example

“I typically use techniques like data sampling for exploratory analysis, dimensionality reduction methods like PCA for feature selection, and data cleaning methods such as missing value imputation and outlier detection. For data storage, I utilize distributed storage systems like HDFS and relational databases like MySQL. I often employ data transformation techniques like MapReduce and Spark for processing large datasets, and I use compression algorithms like gzip for efficient data storage and transfer.”

12. Let’s say we’re building a model to predict real estate home prices in a particular city. We analyze the distribution of the home prices and see that the home’s values are skewed to the right. Do we need to do anything, or is there anything to take into consideration? If so, what should we do?

This question assesses two different things: 1) your understanding of data preprocessing and feature engineering in machine learning and 2) your knowledge of basic statistics. As a data scientist, dealing with skewed data distributions is common, and the ability to handle such data appropriately is crucial for building accurate predictive models.

How to Answer

Discuss the issue of skewed data and its potential impact on the predictive model. Explain the implications of skewed data on model performance and the necessity of addressing it. Then, describe the techniques to handle skewed data, such as logarithmic transformation, square root transformation, or the use of algorithms less sensitive to skewed data.

Example

“When building a model to predict real estate home prices in a particular city, it is crucial to address the issue of the right-skewed distribution of home values. Skewed data can negatively impact the performance of the predictive model, as it can lead to biased predictions. To handle this issue, one approach is to apply a logarithmic or square root transformation to the target variable to make the distribution more symmetrical and reduce the skewness. Another option is to use machine learning algorithms that are less sensitive to skewed data, such as tree-based algorithms.”

13. What is the role of the k value in the k-means algorithm?

An insurance company like GEICO implements many clustering algorithms in many use cases, such as customer segmentation. So, if you’d like to become a data scientist at this company, be sure you are proficient in unsupervised methods for clustering, such as k-means.

How to Answer

Provide a brief overview of the k-means algorithm and its objective. Then, explain the significance of the k value in determining the number of clusters formed by the algorithm. Finally, discuss how the choice of k impacts the quality of clustering results, algorithm convergence, and the balance between model complexity and interpretability.

Example

“The k-means algorithm is an unsupervised machine learning technique for clustering data into k distinct clusters. The k value in the k-means algorithm specifies the number of clusters that the algorithm should identify in the dataset. The choice of k is crucial as it directly influences the number and characteristics of the clusters formed, affecting the algorithm’s convergence and the quality of the clustering results.

A smaller k value may result in broader clusters that fail to capture the details in the data, while a larger k value may produce overly specific clusters, leading to overfitting.”

14. Given a list of tuples featuring names and grades on a test, how can you write a function normalize_grades to normalize the values of the grades to a linear scale between 0 and 1?

This question assesses your proficiency in data manipulation and transformation, fundamental skills required for data preprocessing and feature engineering in machine learning.

How to Answer

Begin by explaining the concept of grade normalization. Then, outline the steps to create the Python function. The function should take the list of tuples as input, extract the grades, and then normalize them to a linear scale between 0 and 1.

Example

“Normalization is a process used to transform values into a common scale, typically between 0 and 1, to facilitate comparisons and analysis. To write the function, we first need to extract the grades from the list of tuples. Then, we can compute the minimum and maximum grades in the list and use them in the normalization formula.

Here’s the Python code to implement the normalization function:”

def normalize_grades(grades):
    # Extract grades from the list of tuples
    grades_list = [grade for name, grade in grades]
    
    # Calculate min and max grades
    min_grade = min(grades_list)
    max_grade = max(grades_list)
    
    # Normalize grades
    normalized_grades = [(name, (grade - min_grade) / (max_grade - min_grade)) for name, grade in grades]
    
    return normalized_grades

15. What is the difference between strong and weak learners in the context of boosting algorithms?

This question assesses your understanding of boosting algorithms, a fundamental machine learning technique used to improve the predictive performance of models by combining multiple weak learners. As a data scientist, understanding the distinction between strong and weak learners is essential for implementing and optimizing boosting algorithms, which are used in predictive modeling within the insurance industry.

How to Answer

Explain the definition of both strong and weak learners in the context of boosting algorithms. Clarify that a weak learner performs slightly better than random guessing and is typically simple and computationally inexpensive, while a strong learner achieves high accuracy and is often more complex and computationally expensive. Conclude by emphasizing that the final model (strong learner) is a weighted combination of the weak learners.

Example

“In the context of boosting algorithms, a weak learner is a model that performs slightly better than random guessing and focuses on minimizing the error. A weak learner can be a regression model or a tree-based model. In boosting algorithms like AdaBoost and Gradient Boosting, weak learners are used to build an ensemble of models where each model corrects the errors of its predecessor. The final model, which is a strong learner, is a weighted combination of these weak learners, achieving high accuracy by leveraging the strengths of multiple weak learners.”

16. Given a list of strings, how can you write a function from **scratch to sort the list in ascending alphabetical order?

At GEICO, you’re expected to deal with data manipulation and transformation during your data analysis project. To do so, you need to be proficient in Python as well as data structures and algorithms. One of the common problems related to data structures and algorithms is how to write a code in the simplest time complexity.

How to Answer

First, explain the basic approach to solving this problem. Use a comparison-based sorting algorithm like Merge Sort or Quick Sort to achieve a time complexity of O(n log(n)). Next, describe the chosen sorting algorithm briefly and then present the code implementation to sort the list without using the built-in sorted function.

Example

“Sorting a list of strings in ascending alphabetical order is a fundamental operation in computer science. One of the efficient comparison-based sorting algorithms that achieves a time complexity of O(n log(n)) is merge sort. To implement the sorting from scratch, we can use a recursive approach to divide the list into smaller sublists, sort them individually, and then merge them back together in sorted order. Below is the Python code to implement the sorting function:”

def merge(left, right):
	 result = []
	 i, j = 0, 0
 
	 while i < len(left) and j < len(right):
		 if left[i] < right[j]:
			 result.append(left[i])
			 i += 1
		 else:
			 result.append(right[j])
			 j += 1
 
	 result.extend(left[i:])
	 result.extend(right[j:])
	 
	 return result

def sorting(array):

	 if len(array) <= 1:
		 return array
	 
	 mid = len(array) // 2
	 left = sorting(array[:mid])
	 right = sorting(array[mid:])
	 
	 return merge(left, right)

array = ["apple", "cat", "banana", "zoo", "football"]
sorted_array = sorting(array)
print(sorted_array) # Output: ['apple', 'banana', 'cat', 'football', 'zoo']

17. How would you deal with outliers in your dataset?

A data scientist at GEICO is expected to master the concept of outliers since they are common in big data. Therefore, demonstrate you know how to handle them during a data science project and whether they should be removed or kept in an analysis.

How to Answer

Mention the importance of identifying and handling outliers to prevent them from skewing the analysis and affecting the performance of predictive models. Discuss various techniques for detecting outliers and the methods to deal with them, including removal, transformation, and the use of robust statistical measures. Remember to mention the importance of understanding the domain and context of the data to make informed decisions when handling outliers.

Example

“Dealing with outliers in a dataset is a crucial step in the data preprocessing phase to ensure the quality and reliability of predictive models. To identify outliers, I typically employ techniques such as visualizations like box plots and scatter plots, statistical methods like Z-score and IQR (interquartile range), and machine learning algorithms like Isolation Forest and DBSCAN.

Once outliers are detected, I use several methods to deal with them, including removing the outliers, transforming the data using techniques like logarithm or square root transformation, and using robust statistical measures like median instead of mean. It is essential to understand the domain and context of the data to make informed decisions when handling outliers, as removing or transforming them without proper justification may lead to loss of valuable information and affect the model’s performance.”

18. Let’s say we have a jar with some balls inside. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls. How can you write a function to simulate drawing balls from the jar?

Knowledge of statistics is another important hard skill to possess if you’d like to become a data scientist at GEICO. As an insurance company, statistics play an important role in many use cases, such as credit risk assessment, claim analysis, and fraud detection. This question, in particular, assesses your Python and multinomial distribution knowledge.

How to Answer

Simulate drawing a ball from the jar based on the given probabilities. First, you need to calculate the probabilities of drawing each color of the ball by dividing the count of each color by the total number of balls. Then, generate a random number between 0 and 1. Iterate through the jar and accumulate the probabilities until the cumulative probability exceeds the random number. The color corresponding to the cumulative probability at which this happens is the color of the ball drawn from the jar.

Example

“To solve this problem, I would calculate the probabilities of drawing each color of the ball by dividing the count of each color by the total number of balls. Then, I would generate a random number between 0 and 1, before iterating through the jar and accumulate the probabilities until the cumulative probability exceeds the random number.

Here’s a Python function to simulate drawing a ball from the jar.”

import random

def sample_multinomial(jar, n_balls):
 total_balls = sum(n_balls)
 probabilities = [n / total_balls for n in n_balls]
 
 rand_num = random.random()
 cumulative_prob = 0
 
 for i, prob in enumerate(probabilities):
	 cumulative_prob += prob
	 if rand_num <= cumulative_prob:
		 return jar[i]

jar = ['green', 'red', 'blue']
n_balls = [1, 10, 2]
result = sample_multinomial(jar, n_balls)

19. What do you know about the concept of confounding variables?

When dealing with data analysis and interpretable machine learning algorithms like linear regression, mastering the concept of confounding variables is important to ensure we can derive the correct interpretation of the model’s prediction.

How to Answer

Start by explaining the definition of confounding variables and their role in data analysis. Discuss how confounding variables can distort the true relationship between the independent and dependent variables, leading to misleading conclusions. Then, provide examples of common confounding variables and explain the methods to identify and control for confounding variables.

Example

“Confounding variables are external factors that can distort the true relationship between the independent and dependent variables, leading to misleading conclusions. For example, in a study examining the relationship between exercise and heart health, age could be a confounding variable as it affects both the level of exercise and the risk of heart disease.

Various methods, such as stratification, matching, and multivariate regression analysis, can be used to identify and control for confounding variables. Carefully evaluating confounding variables is essential to ensuring the validity and reliability of data analysis and predictive modeling results.”

20. How can you write a function that outputs the (sample) variance given a list of integers?

This question evaluates your understanding of basic statistical concepts and your ability to implement those statistical calculations in code.

How to Answer

Explain the concept of variance and its relevance in statistics, particularly in measuring the spread or dispersion of a dataset. Then, outline the steps to compute the sample variance.

Example

“Variance is a statistical measure that represents the spread or dispersion of a set of data points around their mean value. To compute the sample variance for a given list of integers, we first need to calculate the mean of the list. Then, using this mean, we can compute the variance of the list.”

def get_variance(data):
    mean = sum(data) / len(data)
    variance = sum((x - mean) ** 2 for x in data) / (len(data) - 1)
    return round(variance, 2)

test_list = [6, 7, 3, 9, 10, 15]
print(get_variance(test_list))  # Output: 13.89

21. Your company runs an AB test on a landing page feature to increase conversion rates, yielding a .04 p-value. How would you assess the validity of this result?

This question might be asked in a Geico Data Scientist interview to evaluate your understanding of statistical significance and experimental design, crucial for making data-driven decisions. Geico relies heavily on data to optimize processes like customer acquisition and retention.

How to Answer

When answering, clarify the assumptions about the AB test setup first. Focus on how the user groups were separated and whether the variants were equal in all aspects. Then, address the measurement process, considering sample size, test duration, and how the p-value was calculated. Highlight the importance of avoiding pitfalls like continuously monitoring the p-value or stopping the test too early, as these can lead to inaccurate conclusions.

Example

“To assess the validity of the AB test result with a .04 p-value, I would first clarify how the test was set up. I’d check how the user groups were separated to ensure they were sampled properly and that the control and variant groups are comparable. I would also ensure that the variants were equal in all other aspects to avoid external factors skewing the results. Then, I’d evaluate the measurement process—considering the sample size, the duration of the test, and whether the p-value was monitored continuously. This would help me determine if the result is genuinely significant or if there’s a potential for error.”

22. Given a table of students and their SAT test scores, write a query to return the two students with the closest test scores with the score difference.

This question is likely asked to assess a candidate’s ability to work with real-world data scenarios where close numerical values need to be identified and compared. It tests the candidate’s SQL proficiency, particularly their ability to use window functions, sorting, and conditional logic to identify and rank differences between rows.

How to Answer

When answering, focus on using a self-join to compare each student’s SAT score with others, ensuring no duplicates by using a condition like s1.id < s2.id. Highlight the need to calculate the absolute score difference, sort by the smallest difference, and handle ties alphabetically, then limit the result to find the closest pair.

Example

“To answer this question, I would start by explaining that I would use a self-join to compare each student’s SAT score against every other student’s, making sure to avoid duplicates by using a condition like comparing IDs. I would focus on calculating the absolute difference between scores and then sort the results by the smallest difference to identify the closest pair. Additionally, I would handle any tie cases by sorting the students alphabetically, ensuring that the query meets all the requirements.”

As shown by the list of questions in the previous section, the interview process for a data scientist position at GEICO requires a solid foundational knowledge of both technical and behavioral skills. You must demonstrate your skills to increase your chances of getting hired. To help, we’ll provide tips to give you a competitive advantage over other candidates.

Research GEICO’s Core Business

Before submitting your application documents, research GEICO’s mission, values, and the general nuances of the insurance industry. Familiarizing yourself with GEICO’s insurance solutions and how they want to use data to optimize their business can significantly enhance your application.

In fact, check out GEICO’s website to learn more about all the insurance solutions they offer to their customers. Each type of insurance has its own page where you can learn more about them.

Brush-Up Technical Skills

As mentioned, you’ll encounter various questions in each round, with technical questions taking up a big portion of them. So, refresh your knowledge of fundamental data science concepts before the interview process.

At Interview Query, we offer multiple learning paths to assist you in refining your data science expertise, including data science, machine learning, statistics, and probability learning paths.

In the first technical interview round, you’ll tackle coding questions that will test your Python and SQL skills. Accordingly, we offer SQL and Python learning paths to prepare you for such coding challenges. To improve your ability to solve algorithmic questions, check out the question banks available on our platform.

If you find yourself overwhelmed by the breadth of subjects you need to cover, one strategy is to look at the job description. This will help you narrow your learning path and ensure you learn the relevant technical skills for the interview.

Do Personal Projects Related to GEICO’s Domain

Companies seek data scientists committed to continuous learning, particularly in its domain. To distinguish yourself from other candidates, demonstrate your enthusiasm for GEICO by undertaking a personal project related to its domain. This could be in fraud detection, credit risk analysis, claim prediction, etc.

A personal project offers several advantages. First, it showcases your eagerness to contribute to GEICO. Second, it can serve as an engaging discussion point during your interview. Last, it improves your problem-solving abilities as you need to implement various data science concepts throughout the project.

To further hone your problem-solving skills and give ideas on how to conduct a personal project independently, you can explore our take-home challenges. There, you can select a potential topic and solve it on a step-by-step basis using a notebook. If you need some tips to complete take home challenges, feel free to check out our in-depth article that covers this topic.

If you need some ideas about the topic of take home challenges and personal projects, then we also offer resources about the top personal projects and take home challenges that you can do to make you standout from other candidates.

Practice Your Communication Skills

Apart from technical expertise, it’s crucial to hone communication skills. In two of the technical rounds, you’ll encounter a case study-type question in which you need to demonstrate your ability to dissect a problem and articulate your thought process succinctly.

Consider participating in a mock interview with your peers to practice your communication skills. In a mock interview, you’ll have the opportunity to explain concepts and walk people through your thought process in solving a problem. However, finding a peer for a mock interview is challenging since few people have the same passion as we do for data science, making it difficult to receive constructive feedback.

To overcome this challenge, you can join a mock interview service on our platform, connecting you with like-minded data enthusiasts. This way, you and your peers can exchange and receive personalized feedback, improving your interview performance

These are frequently asked questions by individuals interested in working as a data scientist at GEICO.

How much do data scientists at GEICO make in a year?

The base pay for a data scientist position at GEICO is at least $90,000. However, this statistic is currently only based on one data point, meaning the number might differ as more data points are gathered. For comparison, the average base pay for a data scientist position in the industry is between $70,000 and $183,000.

Where can I read more about people’s interview experiences for a data scientist position at GEICO here on Interview Query?

Currently, we do not have a dedicated section for interview experiences specific to a data scientist position at GEICO. Nonetheless, you can read other people’s interview experiences for a data scientist or other data-related positions at various companies in our interview experiences section.

You can also engage with fellow data science enthusiasts or people who pursue data-related roles in the IQ community on Slack to gain insights and tips.

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

We do not directly list job postings for a particular company and role, including a data scientist position at GEICO. If you wish to explore the most recent openings for data scientists or other data-related roles at GEICO, we recommend visiting their official careers page.

If you’re interested in discovering new opportunities for data scientists at various companies, our jobs board provides an updated list of available positions across the globe in the data domain.

And those are the tips that we recommend you to implement straight away. If you’re seeking more detailed tips for your data scientist interview preparation, then you can check out our article dedicated to this.

In this guide, you’ve seen common interview questions in data scientist interviews at GEICO. As mentioned, you must demonstrate that you possess the essential skills, both technical and behavioral.

Beyond the interview questions and tips presented in this guide, you can further refine your technical and interpersonal skills through the plethora of resources available on our platform, such as general data science, Python, SQL, and behavioral interview question examples.

If you’re keen on understanding the interview processes for other data-related roles at GEICO, we’ve got you covered. Check out our GEICO guides for data analyst and software engineer interviews.

We hope that this article helps you prepare for the data scientist interview at GEICO. If you have any questions or need assistance, please contact us on our platform!