As a leading health solutions company, CVS Health strives to deliver personalized health care for millions of people in America. This mission is backed by their data, as 83% of respondents want their primary care provider to know about their personal medical history when they receive care.

With this need for personalized medicine, CVS Health needs data science talents who are able to extract meaningful information from data to improve customer experience – and one of these talents could be you.

If you’re preparing for an interview for a Data Scientist role at CVS, you’re now in the right place. This guide presents several frequently asked CVS Health data scientist interview questions, complete with an example of how you can answer each of them.

So let’s dive in!

The interview process for a Data Scientist position at CVS Health may vary in duration and structure depending on the specific role applied for, but typically consists of several distinct stages, each managed by different teams within the organization.

The interview process begins with a review of your application documents by the recruitment team at CVS Health. During this initial screening round, recruiters assess whether your qualifications, experiences, and skills match the job requirements.

If your qualifications match with job requirements, then you’ll be invited to the first technical round. This first round is normally conducted online, and the format is live coding. This means that you’ll get LeetCode-type of questions that will mostly test your programming and algorithmic skills.

Once you pass the first round, you’ll be invited to a second technical round with a team member from the analytics department. In this round, you’ll be presented with case study-type questions, which means they want to assess both your general problem-solving skills and your thought process when generating solutions.

In the third technical round, you’ll again have the interview with members from the analytics department. However, the interview questions in this round are catered toward data science and machine learning concepts, such as statistics, probability, A/B testing, bias and variance, overfitting and underfitting, etc.

This is the final interview round at CVS Health. In this round, you’ll have an interview with a panel of three people from the analytics department. The type of questions that you need to tackle are a mixture of the questions that you’ve seen in the previous rounds and behavioral questions. There’s also a coding question that you won’t have to fully code out, but will instead will explain the logic on how to solve the problem.

As you can see from the previous section, there’s quite a lot of rounds that you need to pass during the interview process at CVS Health. The questions are also varied, ranging from technical questions to behavioral questions. Therefore, in this section, we’ll delve deep into typical interview questions that you’ll find in a CVS Health’s Data Scientist interview.

When you’re applying for a Data Scientist position at CVS Health, you need to demonstrate your commitment to continuous learning and your ability to stay informed about advancements in data science. Having this trait will make you a very desirable candidate for any position, especially in a rapidly evolving field like data science.

**How to Answer**

Start by discussing various strategies that you use to stay updated with the latest trends and developments in data science. This may include attending conferences, workshops, and webinars, participating in online forums and communities, reading research papers and journals, and taking online courses or certifications. Then, show how your proactive approach to learning benefits you to solve real-world problems.

**Example**

*“To stay up to date with the latest trends and developments, I regularly attend data science conferences such as the IEEE International Conference on Data Mining and the Conference on Neural Information Processing Systems (NeurIPS). I also participate in online communities like Stack Overflow and Reddit’s to learn from peers and share insights. As informal sources of learning, I subscribe to newsletters and follow leading data science blogs and podcasts such as Towards Data Science and Data Skeptic to stay informed about new methodologies and tools. By continuously learning and adapting to emerging trends, I would like to apply the latest advancements in data science to address challenges in my work environment.”*

Any company, including CVS Health, values employees who can collaborate effectively, receive feedback constructively, remain self-aware, and continuously strive for improvement. By asking this question, they want to assess your fit to the company’s culture.

**How to Answer**

You need to provide a balanced response that highlights both your strengths and areas for improvement. Most importantly, you should speak positively about your relationship with your previous manager, emphasizing qualities such as reliability, communication, and collaboration. At the same time, you should acknowledge areas where you have received constructive criticism and demonstrated your willingness to learn and grow.

**Example**

*“In my previous role, I believe my manager would describe me as a dedicated and reliable team member who consistently delivers high-quality work. He has often praised my ability to communicate complex ideas effectively and collaborate with cross-functional teams to achieve project goals. However, I am also receptive to constructive criticism and actively seek feedback to improve. For example, my manager has encouraged me to work on prioritizing tasks more efficiently to manage tight deadlines better. I’ve taken this feedback onboard and have been implementing strategies such as time-blocking and task prioritization to address this area for improvement.”*

Data Scientists at big companies like CVS Health often need to collaborate with colleagues from different departments such as clinical practitioners, IT, and business operations, to ensure that projects meet the organization’s goals and address relevant challenges. Therefore, this question is asked to assess your ability to collaborate effectively with interdisciplinary teams.

**How to Answer**

Start by providing a detailed example of a data science project where you collaborated with teams from different departments within a company. Don’t forget to mention the challenges that you faced throughout the project and the resolution of the problems. Also, discuss the importance of understanding the unique perspectives and requirements of each department to ensure alignment and maximize the project’s impact.

**Example**

*“I was part of the team at a retail company in a project aimed at optimizing inventory management across multiple store locations. This project involved collaborating with teams from the supply chain, sales, and finance departments. My role was to analyze historical sales data, identify demand patterns, and develop a predictive model to forecast product demand accurately.*

*I worked closely with the supply chain team to understand inventory constraints and with the sales and finance teams to incorporate market trends and promotional activities into the forecasting model. By leveraging each department’s expertise and aligning our objectives, we developed a robust forecasting model that reduced excess inventory by 15% and stockouts by 10%, leading to improved sales performance and cost savings for the company.”*

This question is asked to assess your ability to collaborate effectively and resolve conflicts in a team setting, which is crucial for driving successful data science projects at CVS Health. Data Scientists often work with cross-functional teams comprising individuals with diverse backgrounds and perspectives. Therefore, being able to navigate disagreements and foster constructive dialogue is essential for achieving consensus and advancing project objectives.

**How to Answer**

Start by providing a specific example of a time when your colleagues disagreed with your approach on a project. Next, explain how you actively engaged your colleagues, listened to their concerns, and encouraged open dialogue to address differences in perspectives.

**Example**

*“In a previous project at a retail company, my colleagues and I were tasked with optimizing the layout of product displays in stores to improve sales performance. While I advocated for implementing a data-driven approach using customer purchase data and heat mapping analysis, some team members preferred relying on anecdotal evidence and intuition.*

*Recognizing the importance of addressing these differences in opinion, I organized a team meeting to discuss our respective viewpoints openly. During the meeting, I listened attentively to my colleagues’ concerns and provided data-driven evidence supporting the efficacy of our proposed approach. Also, I encouraged team members to share their insights and suggestions for improvement. Through collaborative discussion and compromise, we ultimately agreed to conduct a pilot study that combined elements of both data-driven analysis and traditional methods.”*

The data sets that you need to analyze at CVS Health are huge. As a future CVS Data Scientist you therefore need to understand the concepts of optimizing the query of your SQL statements. Optimizing SQL queries for performance is crucial for efficiently retrieving and analyzing large volumes of data, ensuring timely and accurate insights for decision-making in patient care and operational processes.

**How to Answer**

Start by mentioning common techniques for optimizing SQL queries, such as using appropriate indexing, minimizing the use of wildcard characters, avoiding unnecessary joins or subqueries, and optimizing data retrieval by filtering and sorting efficiently. Then, also mention the importance of testing query performance using tools like EXPLAIN or Query Execution Plans and iterating on optimizations based on profiling results.

**Example**

*“To optimize SQL queries for performance, I focus on several key techniques. First, I ensure appropriate indexing on columns frequently used in WHERE clauses or join conditions to minimize data scanning. Second, I avoid using wildcard characters at the beginning of search strings to leverage indexes efficiently. As an addition, I also normally optimize query logic by reducing unnecessary joins and subqueries, and I use window functions or common table expressions (CTEs) to simplify complex queries. Last, I regularly test query performance using tools like EXPLAIN or Query Execution Plans and refine optimizations based on profiling results.”*

This question is asked in a CVS Health Data Scientist interview to assess your proficiency in SQL, a fundamental skill for Data Scientists working with databases. Having proficiency in SQL is essential for you to be able to extract meaningful insights from relational databases in a quick and timely manner.

**How to Answer**

Start by reading the questions carefully and check the output that the question expects. The query should join the ‘users’ and ‘rides’ tables on the appropriate keys and aggregate the distance traveled by each user. Don’t forget to make sure that the distance traveled is reported in descending order to meet the specified requirement.

**Example**

*“To solve this problem, I would write an SQL query that joins the ‘users’ and ‘rides’ tables on the user_id column and calculates the total distance traveled by each user using the SUM() function. The results are then grouped by user name and ordered in descending order of distance traveled.”*

```
SELECT SUM(r.distance) AS distance_traveled, u.name
FROM users u
LEFT JOIN rides r ON u.id = r.passenger_user_id
GROUP BY u.name
ORDER BY distance_traveled DESC;
```

In healthcare analytics like CSV Health, dealing with large datasets is common. Thus, identifying unique features can help in developing more effective models for tasks like patient diagnosis, treatment recommendation, or resource allocation. This question assesses your ability to handle high-dimensional data and extract meaningful features from noise.

**How to Answer**

You should clearly demonstrate your understanding of common feature reduction techniques applied in data science such as principal component analysis (PCA) or t-SNE. Another alternative is by describing common feature selection methods like recursive feature elimination (RFE); or even by using domain knowledge to filter out irrelevant features if the healthcare area is your domain of interest.

**Example**

*“We can reduce 500 features to unique features by using techniques such as:*

*Principal Component Analysis (PCA): PCA transforms high-dimensional data into a lower-dimensional space while preserving most of the variance. By selecting a subset of principal components that capture the most variation in the data, we can effectively reduce dimensionality.**Feature Selection Algorithms: Techniques like Recursive Feature Elimination (RFE), SelectKBest, or Lasso Regression can be used to rank features based on their importance and select the most informative ones. These algorithms iteratively remove or select features based on certain criteria, such as statistical significance or predictive power.**Domain Knowledge Integration: domain knowledge will definitely help to identify relevant features that can improve a model’s performance.”*

When you’re applying for a Data Scientist position at CVS Health, your knowledge of model evaluation and validation techniques is important. This is because CVS Health may use predictive models for various purposes, such as estimating medication delivery times or optimizing patient appointment scheduling.

**How to Answer**

Start by describing a structured approach to evaluating the performance of the new delivery time estimate model compared to the old model. To do this, you should discuss relevant metrics for model evaluation, such as mean absolute error (MAE) or root mean squared error (RMSE), and propose a methodology for conducting a comparative analysis. Don’t forget to also mention the importance of using appropriate validation techniques, such as cross-validation or holdout validation, to ensure the reliability of the evaluation results.

**Example**

*“To determine if the new delivery time estimate model predicts delivery times better than the old model, I would first define appropriate evaluation metrics to assess predictive performance, such as mean absolute error (MAE) or root mean squared error (RMSE). I would then collect data on actual delivery times for a representative sample of orders and use both the old and new models to generate delivery time estimates for these orders.*

*Next, I would calculate the MAE or RMSE for each model based on the disparity between the estimated and actual delivery times. Finally, I would conduct statistical tests, such as a paired t-test or Wilcoxon signed-rank test, to compare the performance of the two models and determine if the improvement in predictive accuracy observed with the new model is statistically significant. If necessary, I would like to use cross-validation or holdout validation techniques to assess model performance on independent datasets.”*

Multicollinearity can significantly affect the accuracy and interpretability of a regression model. Therefore, if you’d like to be a Data Scientist at CVS Health, understanding one of the most important concepts like multicollinearity is crucial to ensure the reliability of your analyses and recommendations.

**How to Answer**

First, start by mentioning the definition of multicollinearity and its impact on regression models. Then, explain how multicollinearity increases standard errors, which make coefficients unstable and difficult to interpret. Finally, discuss techniques for detecting multicollinearity and strategies for addressing it, such as variable selection, regularization methods, or principal component analysis.

**Example**

*“Multicollinearity can have a significant impact on the reliability of regression models by inflating standard errors and making coefficient estimates unstable. Let’s say we’re working in healthcare analytics, multicollinearity is particularly relevant due to the complex interrelationships among various health factors. As an example, if we’re modeling patient outcomes and two predictor variables are highly correlated, such as BMI and waist circumference, multicollinearity can lead to false conclusions about the individual effects of these variables on the outcome.*

*To address multicollinearity, we can employ techniques like variable selection, where we choose a subset of predictors with low collinearity, or regularization methods like ridge regression that penalize large coefficient estimates.”*

When you’re applying for a Data Scientist position at CVS Health, you should possess a good proficiency in programming and the ability to manipulate and analyze data using Python. CVS Health deals with large volumes of healthcare data, and you need to be proficient in writing efficient and accurate code to perform various data processing and analysis tasks.

**How to Answer**

To solve this problem, first create a function that calculates the standard deviation of each list of integers in the input list of dictionaries. You need to also try to implement the standard deviation calculation algorithm without using NumPy’s built-in functions, and ensure that the function returns a dictionary with the standard deviation of each list.

**Example**

*“To solve this problem, I would write a function that iterates over each dictionary in the input list, calculates the mean and variance of the list of integers, and then computes the standard deviation using the formula. The function returns a dictionary with the standard deviation of each list of integers.”*

```
def compute_deviation(input):
output = {}
for item in input:
key = item['key']
values = item['values']
n = len(values)
mean = sum(values) / n
variance = sum((x - mean) ** 2 for x in values) / n
std_dev = variance ** 0.5
output[key] = round(std_dev, 2)
return output
input = [
{
'key': 'list1',
'values': [4, 5, 2, 3, 4, 5, 2, 3],
},
{
'key': 'list2',
'values': [1, 1, 34, 12, 40, 3, 9, 7],
}
]
output = compute_deviation(input)
```

Healthcare analytics companies like CVS Health rely on machine learning models that can be easily interpreted. Therefore, if you wish to become a Data Scientist at this company, you need to know various interpretable machine learning algorithms like linear regression as well as the implications of violating assumptions needed to ensure the reliability of these models.

**How to Answer**

First, start by explaining the concept of normality in the context of linear regression and discuss the consequences of its violation. Then, mention common techniques for assessing normality, such as visual inspection of residuals or statistical tests like the Shapiro-Wilk test. Finally, discuss alternative modeling approaches or robust regression techniques that can be used if normality assumptions are not met.

**Example**

*“In linear regression, normality refers to the assumption that the residuals (the differences between observed and predicted values) are normally distributed. If normality doesn’t hold, it implies that the errors in the model are not normally distributed, which can lead to biased coefficient estimates and inaccurate hypothesis testing results.*

*To address this issue, we can visually inspect the distribution of residuals or conduct statistical tests like the Shapiro-Wilk test. If normality assumptions are violated, we may consider alternative modeling approaches such as robust regression techniques like Huber regression or quantile regression, which are less sensitive to deviations from normality.”*

This question is asked in a Data Scientist interview at CVS Health to assess two things:

1) Your ability to manipulate different data structures using Python

2) Your knowledge of a common data preprocessing procedure before machine learning model training.

**How to Answer**

Make sure to understand the question first: take a look at the format of the input and the expected output from the function. Then, write a Python function that splits the input string of dictionaries into two lists, one for training and one for testing, with a 70:30 split. You should use Python string manipulation techniques to parse the input string into a list of dictionaries and then randomly assign each dictionary to either the training or testing set. Finally, make sure that the function returns the two lists as specified in the question.

**Example**

*“To solve this problem, I would write a function that parses the input string into a list of dictionaries using a library called* .

`ast`

```
import ast
import random
def read_split_from_str(list_of_dict_str):
# Parse the string into a list of dictionaries
data = ast.literal_eval(list_of_dict_str)
# Shuffle the data
random.shuffle(data)
# Calculate the split index
split_index = int(len(data) * 0.7)
# Split the data into training and testing sets
training_set = data[:split_index]
testing_set = data[split_index:]
return [training_set, testing_set]
list_of_dict_str = "[{'x': 0.0, 'y': 5.43}, {'x': 50.0, 'y': 102.78}, {'x': 100.0, 'y': 204.24}]"
output = read_split_from_str(list_of_dict_str)
```

Experimental design is a common procedure to find in healthcare analytics like CVS Health to assess the effectiveness of interventions, treatments, or changes in healthcare delivery processes. Therefore, familiarity with A/B testing and the necessary statistical concepts is crucial for Data Scientists to know.

**How to Answer**

First, start by explaining the definition of A/B testing. Then, discuss the statistical concepts required to conduct and analyze A/B tests. This may include understanding hypothesis testing, sample size determination, randomization, and the interpretation of confidence intervals and p-values.

**Example**

*“A/B testing is an experimental method used to compare two versions of a product, intervention, or process to determine which one performs better. To conduct A/B testing effectively, we need to understand statistical concepts such as hypothesis testing, randomization, sample size determination, and interpreting confidence intervals and p-values.*

*For example, when designing an A/B test to compare two medication dosages, it’s essential for us to ensure that patients are randomly assigned to treatment groups to minimize bias. Then, we assess statistical significance between two groups using appropriate methods.”*

This question is asked in a Data Scientist interview at CVS Health to assess two things:

1) Your understanding of statistical concepts, which in this case is the mode.

2) Your programming ability to manipulate data using Python.

**How to Answer**

Make sure that you understand the question, the input, and the expected output of the function that you are about to write. Write a Python function that determines the mode(s) of a list of integers by counting the frequency of each unique value in the list and identify the value(s) with the highest frequency. If there are multiple modes, make sure that the function returns them in ascending order.

**Example**

*“To solve this problem, I would write a function that calculates the frequency of each unique value in the input list using a dictionary. It then identifies the mode(s) with the maximum frequency and returns them in ascending order.”*

```
def mode(nums):
# Count the frequency of each unique value in the array
freq_dict = {}
for num in nums:
if num in freq_dict:
freq_dict[num] += 1
else:
freq_dict[num] = 1
# Find the maximum frequency
max_freq = max(freq_dict.values())
# Find the mode(s) with the maximum frequency
modes = [key for key, value in freq_dict.items() if value == max_freq]
# Sort the modes in ascending order
modes.sort()
return modes
nums1 = [1, 2, 2, 3, 4]
nums2 = [1, 1, 2, 2]
print(mode(nums1)) # Output: [2]
print(mode(nums2)) # Output: [1, 2]
```

This question is asked to assess your knowledge of machine learning concepts. Resampling techniques such as bootstrapping or bagging are concepts that a Data Scientist at CVS Health needs to understand, as the company uses these techniques to improve the robustness and accuracy of predictive models in healthcare analytics applications.

**How to Answer**

Start by explaining the definitions of bootstrapping, and of bagging, then highlight the key differences between the two techniques. You should also discuss the advantages and potential applications of each method, preferably in the area of healthcare analytics.

**Example**

*“Bootstrapping is a resampling technique where multiple samples are drawn with replacement from a single dataset to estimate the sampling distribution of a statistic. In contrast, bagging is an ensemble learning method that involves training multiple independent models on bootstrapped samples of the dataset, then combining their predictions through averaging or voting.*

*Let’s take an example in healthcare analytics, where we want to predict patient readmission rates. In this case, bootstrapping could be used to estimate the uncertainty in the model’s performance metrics, while bagging could be employed to train multiple predictive models on bootstrap samples to improve predictive accuracy by reducing variance.”*

Data Scientists at CVS Health should possess a good understanding of statistics and programming. Therefore, in this question, they want to assess two things from you:

1) Your understanding of basic probability concepts.

2) Your ability to translate that concept into a working Python code.

**How to Answer**

Read the instructions carefully and make sure that you understand the provided inputs and expected output. Then, write a Python function that simulates a specified number of coin tosses with a given probability of heads. You should use random number generation techniques to simulate the outcomes of individual coin tosses based on the provided probability. Finally, make sure that the function returns a list of the randomly generated results, where ‘H’ represents heads and ’T’ represents tails.

**Example**

*“To solve this problem, I would write a function that simulates a specified number of coin tosses based on the provided probability of heads. It iterates through the number of tosses and generates a random outcome for each toss using random.random(). If the generated random number is less than the probability of heads, ‘H’ (heads) is appended to the outcomes list; otherwise, ’T’ (tails) is appended. Finally, the function returns the list of outcomes representing the results of the coin tosses.”*

```
import random
def coin_toss(tosses, probability_of_heads):
outcomes = []
for _ in range(tosses):
outcome = 'H' if random.random() < probability_of_heads else 'T'
outcomes.append(outcome)
return outcomes
tosses = 5
probability_of_heads = 0.6
print(coin_toss(tosses, probability_of_heads)) # Output: ['H', 'T', 'H', 'H',
```

Understanding statistics is crucial for Data Scientists at CVS Health, as you’ll be dealing with healthcare data all the time and conduct a comprehensive analysis on it. Central Limit Theorem (CLT) is essential for understanding the behavior of sample means and justifying the use of parametric statistical tests in any analytics use case.

**How to Answer**

Start by providing a concise definition of the Central Limit Theorem and explain its significance in statistical inference. Then, also discuss how the CLT states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution. Finally, don’t forget to highlight the implications of the CLT for hypothesis testing and confidence interval estimation in healthcare analytics.

**Example**

*“The Central Limit Theorem (CLT) states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution. For example, when analyzing patient data to compare treatment outcomes, the CLT allows us to make valid inferences about the population parameters using popular statistical tests, such as t-tests or ANOVA, even if the population distribution is non-normal.”*

The understanding of probability concepts and the ability to apply them to real-world scenarios are essential skills for Data Scientists at CVS Health. When dealing with healthcare data, you often need to calculate the likelihood of certain events occurring, or the effectiveness of certain experimentations, and all of these require a good proficiency in statistics and probability.

**How to Answer**

Start by identifying the possible outcomes of the scenario and their associated probabilities. In this case, there are two possible outcomes: both flips result in heads or both flips result in tails. Then, calculate the probability of each outcome and determine the probability that both flips result in the same side by summing the probabilities of the two favorable outcomes.

**Example**

*“To solve this problem, we first need to identify the possible outcomes of flipping the two coins: both flips result in heads (HH) or both flips result in tails (TT). Given that we select a coin at random, the probability of each outcome depends on the probability of selecting each coin and the probability of flipping heads or tails with each coin.*

*Let P(H) denote the probability of flipping heads and P(T) denote the probability of flipping tails. Then, the probability of selecting the fair coin is ^{1}⁄_{2}, and the probability of selecting the biased coin is also ^{1}⁄_{2}. For the fair coin, P(H) = ^{1}⁄_{2} and P(T) = ^{1}⁄_{2}. For the biased coin, P(H) = ^{3}⁄_{4} and P(T) = ^{1}⁄_{4}.*

*Therefore, the probability that both flips result in the same side can be calculated as follows:*

*P(same side) = (P(fair coin) * (P(HH | fair coin) + P(TT | fair coin) ) ) + (P(biased coin) *(P(HH | biased coin) + P(TT | biased coin)))*

*P(same side) = (½ * ( ^{1}⁄_{4} + ^{1}⁄_{4} )) + (^{1}⁄_{2} *(^{9}⁄_{16} + ^{1}⁄_{16}))”*

This question is asked to evaluate your understanding of regression analysis, a fundamental statistical technique used extensively in healthcare analytics-based businesses like CVS Health. R-squared is a commonly used metric in regression analysis to assess the goodness of fit of a model to the data. Therefore, having this knowledge is essential if you’d like to become a Data Scientist.

**How to Answer**

Start by mentioning the definition of R-squared and explain its interpretation in the context of regression analysis. Then, segue to discussing how R-squared represents the proportion of variance in the dependent variable explained by the independent variables in the model, with higher values indicating a better fit. If necessary, highlight common use cases for R-squared, such as model comparison or assessing model performance.

**Example**

*“In regression analysis, R-squared is a statistical measure that represents the proportion of variance in the dependent variable explained by the independent variables in the model. Let’s say we have a model to estimate patient outcomes based on clinical variables. A higher R-squared value indicates that the model is able to capture a large proportion of variability in the outcome, suggesting that we can trust the result from the model. We normally use R-squared to compare different models, evaluate model performance, and communicate the strength of relationships between predictors and outcomes.”*

This question assesses your ability to use SQL to analyze healthcare data.

**How to Answer**

To solve this question, you should write an SQL query that retrieves all dates when the hospital released more patients than the day prior. You can use SQL functions such as LEAD or LAG to compare the number of released patients on consecutive days and filter the results accordingly. Finally, make sure that the query accounts for potential edge cases, such as the first day of the dataset or days with no patient releases.

**Example**

*“To solve this problem, I would write a SQL query with the inner subquery that calculates the number of released patients for each day and retrieves the number of released patients on the previous day using the LAG window function. The outer query then filters the results to only include dates where the number of released patients is greater than the number on the previous day. This query will effectively identify all dates where the hospital released more patients than the day prior.”*

```
SELECT release_date, released_patients
FROM (
SELECT release_date, released_patients,
LAG(released_patients) OVER (ORDER BY release_date) AS prev_patients
FROM released_patients
) AS subquery
WHERE prev_patients IS NOT NULL AND released_patients > prev_patients;
```

Understanding the principles of probability and their application to real-world problems is crucial for Data Scientists at CVS Health. Analyzing healthcare data often involves estimating the likelihood of various events or the effectiveness of interventions, all of which require strong statistical and probabilistic skills.

**How to Answer**

Begin by establishing the theoretical framework and applying it to the scenario. In this case, flipping a coin 10 times with 8 tails and 2 heads requires us to determine if the coin is fair using the binomial distribution. The probability of observing such an outcome under the assumption of a fair coin needs to be calculated.

**Example**

*“To determine if the coin is fair, we model the coin flips using a binomial distribution, where each flip is a Bernoulli trial with two possible outcomes: heads or tails. For a fair coin, the probability of heads (p) is 0.5. The binomial probability formula is given by P(x successes) = (n choose x) x p^x x (1-p)^(n-x), where n is the number of trials, and x is the number of successes.*

*In this scenario, n = 10 and x = 2 (for heads). Therefore, we need to calculate P(X=2) for a fair coin:*

P(X=2) = (10 choose 2) * (0.5)^2 * (0.5)^8 = 45 * (0.25) * (0.00390625) ≈ 0.0439

*This probability is quite low, suggesting that observing only 2 heads out of 10 flips is unlikely if the coin is fair. To further investigate, we can check if a different value of p (less than 0.5) makes this outcome more probable. For instance, if p = 0.4:*

P(X=2) = (10 choose 2) * (0.4)^2 * (0.6)^8 ≈ 0.1209

*This higher probability indicates that a coin biased towards tails might better explain the observed outcome. Therefore, statistical analysis suggests the coin may not be fair, and further tests or larger sample sizes could provide additional insights.”*

Understanding the mechanism of random forest generation and its advantages over other algorithms is essential for Data Scientists at CVS Health. Utilizing such models appropriately can significantly enhance predictive analytics and decision-making processes in healthcare.

**How to Answer**

Start by explaining the basic concept of a random forest and how it is constructed. Then, compare its performance and suitability to other algorithms like logistic regression, emphasizing the contexts in which random forests are particularly advantageous.

**Example**

*“Random forests are an ensemble learning method that constructs multiple decision trees during training and merges their results to improve accuracy and prevent overfitting. Each decision tree is built by considering different subsets of the data and features, ensuring diversity among the trees. For example, when diagnosing diseases, each tree might focus on different medical test results or patient histories, capturing various aspects of the data.*

*Compared to logistic regression, which models the probability of a categorical outcome using a logistic function, random forests excel in handling datasets with complex interactions and non-linear relationships. They are particularly useful when individual features have varying levels of importance across different instances. For instance, a patient’s age might be crucial in some diagnoses but irrelevant in others. Random forests can handle such variability better than logistic regression, which assumes a linear relationship between features and the outcome.*

*Moreover, random forests are robust to noise and overfitting due to their ensemble nature, making them a reliable choice for complex and high-dimensional healthcare datasets. By leveraging the collective decision of multiple trees, random forests provide more accurate and generalized predictions compared to single models like logistic regression.”*

As you’ve seen from the list of questions above, the interview process at CVS Health for a Data Scientist position demands a strong understanding of both technical and behavioral skills – and you need to convincingly show them that you possess those skills. Therefore, in this section, we’ll give you several tips that will give you that competitive edge over other candidates.

This is the most fundamental thing that you need to do, even before you submit your application documents. By researching and understanding CVS Health’s mission, values, and everything related to the healthcare industry in general, you can create more personalized applications.

In fact, you can check out the insight studies that they’ve conducted to familiarize yourself with CVS Health’s data-driven initiatives and how they want to shape their business with data. There, you’ll find different use cases of how data analytics help them to understand their customer better and build a better product for them.

As you’ve seen from the previous section, you’ll get a wide variety of questions within each round, where technical questions take a big portion of them. Therefore, it’s essential for you to refresh your knowledge of fundamental data science concepts before the interview process.

Here on Interview Query we have several learning paths that will help you to brush-up on your data science concepts, including data science, machine learning, statistics, and probability learning paths.

Also, in the first round of technical interview, you need to solve coding questions that test your SQL and Python skills. Thus, you also need to practice your programming skill and we also have SQL and Python learning paths that you can follow to prepare yourself for such coding interviews. To improve your ability to solve algorithmic questions, make sure to check out the question banks available on our site.

If you feel overwhelmed by the wide variety of subjects that you need to learn, a good tip is to look at the job description. There you can identify the key skills required for the role and you can start practicing the concept and the tools mentioned there.

One desirable trait of a Data Scientist is the commitment to continuous learning, especially in the domain of the company that you’re applying for. To give you the edge over other candidates, you need to show your enthusiasm for applying at CVS Health by conducting a personal project related to their domain, which in this case is healthcare analytics.

A personal project provides several advantages to you. First, it shows your eagerness to be able to contribute to their company in the future. Second, it can serve as an interesting talking point during your interview. Third, it will enhance your problem-solving skills as you need to implement several data science concepts along the process.

To further exercise your problem-solving skills as well as to give you ideas on how to conduct a personal project on your own, you can first check out our take home challenges. There you can choose a possible topic and solve it step-by-step with a notebook.

Aside from technical skills, you need to also practice your communication skills. This is because you’ll get an interview with a case study-type question in two of the technical rounds. There, you need to show your ability to dissect a problem, and then explain your thought process in a concise manner. Without proper practice, this can break your chance of getting hired.

In order to practice your communication skills, you can conduct a mock interview with your peers. In a mock interview, you’ll get a chance to practice how to properly explain concepts and walk people through your thought process of solving a problem. The problem is, most of us don’t have peers who are also passionate about data science and therefore, they’re unable to give us proper feedback.

To solve this problem, you can join a mock interview service available on our site, where you’ll be connected with fellow data enthusiasts. This way, you and your peers can give and receive personal feedback to each other.

These are some of the frequently asked questions by people interested in working as a Data Scientist at CVS Health.

$140,643

Average Base Salary

$151,323

Average Total Compensation

The base pay for a Data Scientist position at CVS Health ranges between $100k to $180k, depending on your work experience. As a comparison, the average base pay for a Data Scientist position in general ranges between $70k to $183k.

You can read other people’s interview experiences for CVS Health Data Scientist or any other data-related positions in our interview experiences section.

You can also interact with other analytics enthusiasts or people who are seeking data-related positions in the IQ community on Slack.

You can look at the list of available positions on our jobs board, or apply to their website directly for current opportunities.

We hope that this article is helpful to you in your preparation for the interview process for the Data Scientist position at CVS Health!

We also recommend other resources that will be useful for your interview prep such as our general data science, case study, data project, Python, and SQL interview questions.

Lastly, if you’re interested in knowing the interview process of other data-related positions at CVS Health, feel free to check them out on our site, as we have covered their Data Analyst, Data Engineer, Business Analyst, Business Intelligence, and Software Engineer interview guides for you.

We wish you luck on your job search!