DraftKings Data Scientist Interview Questions + Guide in 2024

As one of the leading companies in the sports betting and fantasy sports industry, securing a position as a Data Scientist at DraftKings can be very competitive and challenging.

If you’re currently gearing up for your interview and seeking questions that closely resemble those typically asked for a Data Scientist role at DraftKings, then you’re in the right place.

In this guide, we’ll discuss several commonly asked interview questions for the Data Scientist position at DraftKings, along with example responses to help your interview preparation process. Additionally, we’ll share valuable tips at the end to help you gain that competitive edge over other candidates.

So, without further ado, let’s dive in!

Although the length of the interview process may vary depending on the role that you apply for, the typical selection process at DraftKings for a Data Scientist position involves four different rounds, and each round will be conducted by different teams.

Here are the four rounds:

1. Application and Resume Screening

The interview process at DraftKings begins with the resume screening. Here the recruiters will check whether your qualifications, past experiences, and skill sets match with the job description they posted. The typical waiting time for you in this stage would be around 10 business days after you submit your application documents. If your qualifications meet their criteria, then you’ll be invited to the next stage.

2. Phone Interview with HR

Once they check your application documents and think you would be a good fit for the role, they will invite you to a short phone meeting with one of the recruiters. This interview process typically only lasts about 15 minutes and you will be asked to elaborate more on the specific details in your resume that they want to know, as well as your motivation and career goals in the future.

3. First Technical Round

If you pass the phone interview round, they will give you an invitation to the first technical round with a couple of team members from the department that you’ll be working in. This round will typically last 30 minutes and they are normally conducted online.

In this round, you’ll get an assessment consisting of problem-solving type of questions that will test your knowledge of statistics, probability, and coding skills. DraftKings normally uses a tool like Hackerank to assess your skills in this round.

4. Second Technical Round

If you pass the first technical round, they will invite you to the second technical round, which is the last interview process at DraftKings. The content of the second technical round is more or less the same as the first one. However, in this round, you’ll get a business case study type of test.

This means that you’ll be presented with a data science problem that is relatable to the problem you’ll encounter daily when working at DraftKings. They will give you time to solve the problems and then you need to present the solution to them and walk them through your thought process of how you came up with it. In this round, it’s also expected that you’ll get behavioral questions, in which they want to assess your communication and interpersonal skills.

Now that you know what the interview process looks like, let’s dive into the typical questions you’ll find in a Data Scientist interview at DraftKings.

The behavioral questions will mainly assess your motivations and your approach to tackling daily problems with regard to your tasks and collaboration with coworkers. Meanwhile, the technical questions will assess your knowledge in terms of programming and statistics, which you will use a lot in your daily tasks as a Data Scientist at DraftKings.

1. How do you communicate statistical findings to non-technical stakeholders?

The ability to effectively communicate technical findings to non-technical stakeholders is crucial for Data Scientists at DraftKings. In the sports betting industry, data-driven insights play a significant role in decision-making processes, and we must be able to convey complex statistical concepts and analyses in a clear and understandable manner to bridge the gap between data analysis and decision-making.

How to Answer

Start by mentioning your specific approach to communicating statistical findings to non-technical stakeholders, emphasizing clarity, relevance, and simplicity. Next, discuss your method to convey complex statistical concepts in an accessible manner and highlight the importance of understanding stakeholders’ needs and tailoring the communication style and content to our audience.

Example

“When communicating statistical findings, I prioritize clarity and simplicity to ensure that complex concepts are easily understandable to stakeholders, even if they don’t have the technical expertise. I often use data visualization techniques such as charts, graphs, and dashboards to illustrate key insights and trends visually. Also, I always tried to frame statistical findings within a compelling narrative or story with no technical jargon to engage stakeholders and make the findings more relatable and memorable.”

2. How would you prioritize deadlines and manage multiple deadlines?

In a fast-paced and dynamic environment like what you normally see in the sports analytics industry like DraftKings, oftentimes, you’ll find yourself working on projects with different deadlines simultaneously. Therefore, it is important that you demonstrate strong organizational skills and effective time management techniques that show your readiness to handle the demands of the projects.

How to Answer

First, start by mentioning your usual approach to prioritizing deadlines and managing multiple tasks. Don’t forget to mention that you like to implement techniques such as setting clear priorities, breaking down tasks into smaller actionable steps, and using time management tools or techniques (e.g., to-do lists and calendars) in order to do so. Next, mention your ability to adapt to changing priorities and stay organized under pressure.

Example

“To prioritize deadlines and manage my workload efficiently, I usually implement several things. First, I assess the urgency and importance of each task and prioritize them accordingly. I break down larger projects into smaller, manageable tasks and create a detailed timeline or schedule to ensure I stay on track. I use time management tools to keep track of deadlines and allocate sufficient time for each task. In situations where deadlines overlap or priorities shift, I would try to remain flexible and adapt my approach as needed to meet the evolving demands.”

3. How do you stay up to date with the latest trends in our industry?

As you already know, sports analytics is a rapidly evolving field and Data Scientists at DraftKings need to stay informed about emerging techniques, methodologies, and industry trends to keep the competitive edge of the company.

How to Answer

Start by acknowledging the importance of continuous learning as a Data Scientist. Next, discuss your strategies for staying up to date with the latest trends in the sports betting industry. This can be done by regularly reading industry publications, attending conferences, participating in online forums or communities, taking relevant courses or certifications, and networking with industry professionals. Don’t forget to emphasize your willingness to explore new ideas and technologies.

Example

“I know that staying informed with the latest trends is very important as a Data Scientist. To stay up to date with sports analytics, I regularly read industry publications, such as sports analytics journals and online blogs. Sometimes I like to attend conferences and workshops related to sports analytics and data science, where I can network with industry professionals and gain insights from leaders in the field. I also take advantage of online courses and certifications to deepen my understanding of relevant topics and acquire new skills. By staying proactive and curious, I ensure that I am well-equipped to leverage the latest trends and technologies to drive innovation and success in my role as a Data Scientist.”

4. Tell me about the time when you exceeded expectations: what did you do, and how did you accomplish it?

This question assesses your motivation and ability to deliver exceptional results in your daily tasks later on as a Data Scientist, which is crucial for success in a competitive and results-driven environment. Also, this trait will make you become a valuable asset for the organization’s growth and success.

How to Answer

Start by describing a specific example where you exceeded expectations in your previous role by highlighting the actions you took and the strategies you employed to accomplish it. Also, don’t forget to mention the challenges you faced and the innovative approaches or techniques you used to surpass expectations.

Example

“In my previous role, I was tasked with developing a predictive model to forecast player performance in a new sports category with limited historical data. Despite the challenges, I exceeded expectations by conducting extensive research to identify alternative data sources and engineered new features to enhance our inhouse machine learning model performance. Also, I collaborated closely with domain experts to gain insights into the unique dynamics of the sports category. As a result of these efforts, I was able to develop a highly accurate predictive model that outperformed expectations and provided valuable insights for strategic decision-making.”

5. Can you tell us the time when your analysis led to a significant change in how your company makes decisions?

In the fast-paced environment of the sports betting industry, Data Scientists at DraftKings play a crucial role in providing actionable insights that inform strategic decision-making. People at DraftKings want to know your potential to add value and drive innovation.

How to Answer

To answer this question, describe a specific example in your past experience where your data analysis resulted in a substantial change in decision-making. Specifically, you should outline the problem you tackled, the approach that you implemented, the insights you uncovered, and the resulting impact on the organization’s decisions or operations.

Example

“In my previous role, I conducted an in-depth analysis of customer behavior using machine learning models. Through this analysis, I identified key patterns and trends in customer preferences and engagement that were previously overlooked. I presented these findings to senior management, highlighting opportunities for optimizing marketing strategies and enhancing customer retention efforts. As a result of my analysis, the company implemented targeted marketing campaigns and personalized recommendations, leading to a significant increase in customer satisfaction and retention rates.”

6. What do you know about the logistic and softmax functions, and what are the differences between the two?

When applying for a Data Scientist position at DraftKings, you need to understand the fundamental concepts of machine learning. It’s essential that you understand the difference between the two functions and in which scenario we can apply them.

How to Answer

To answer this question, you need to provide a concise explanation of logistic and softmax functions by highlighting their properties and applications in machine learning. Next, you also need to discuss the differences between the two functions, for example, by focusing on their mathematical formulations or the types of problems they are suited for. Finally, mention in which scenario each function is typically used.

Example

“The logistic function is commonly used in binary classification tasks to map input values to a range between 0 and 1, representing the probability of belonging to a particular class. On the other hand, the softmax function is used in multi-class classification tasks to compute the probabilities of each class, ensuring that the sum of probabilities across all classes is equal to 1. The main difference between the two functions lies in their mathematical formulations and the types of classification problems they are suited for. As an example, if we want to predict whether an email is spam or not, we can use a logistic function. Meanwhile, if we want to predict the class of an image of a fruit, where each image can be an orange, an apple, or a mango, then we need to use a softmax function.”

7. What is the expected value of a die roll?

This question assesses your understanding of the basic probability concept, which is essential for a Data Scientist working in the sports betting industry. DraftKings relies on probability theory to model and predict outcomes of their product, and the expected value is a fundamental concept in probability analysis crucial for making informed decisions and developing effective betting strategies.

How to Answer

To answer this question, first start by mentioning the general concept behind the expected value. Then, generalize how the concept is applicable to a die roll scenario. Explain that the expected value of a die roll is the average outcome that would occur over many rolls.

Example

“Expected value can also be called the measure of the mean, which in probability theory tells us about the average outcome of a random variable over many trials. Therefore, we can say that the expected value of a die roll represents the average outcome we would expect over many rolls. Since each face of a fair die has an equal probability of (¹⁄₆), we can calculate the expected value by summing the products of each possible outcome (1 through 6) and its probability. Therefore, the expected value of a die roll is (¹⁄₆) * 1 + (¹⁄₆) * 2 + … + (¹⁄₆) * 6, which simplifies to 3.5.”

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

This question assesses your ability to manipulate and preprocess data, especially data normalization. Normalizing data to a linear scale between 0 and 1 is a common preprocessing step in data science projects before building machine learning models.

How to Answer

To answer the question, first you need to make sure to brush up on your Python skills first, and then read the question carefully. You need to pay attention to what kind of inputs that the function expects and what the output of the function would be. Next, explain the steps involved in normalizing the grades, which typically include calculating the minimum and maximum values and applying a linear transformation to scale the values between 0 and 1.

Example

“First, let’s create a function that takes a list of tuples as input, where each tuple contains a name and a grade. Inside of the function, first we need to fetch the maximum and minimum grades contained in the list and then normalize each grade to a linear scale between 0 and 1 using a linear transformation based on minimum and maximum values. Finally, it returns a list of tuples with the normalized grades. Below is the code implementation to solve this problem:”

def normalize_grades(grades):

# Extract grades from the list of tuples

grades_list = [grade for _, grade in grades]

# Calculate the minimum and maximum grades

min_grade = min(grades_list)

max_grade = max(grades_list)

# Normalize grades to a linear scale between 0 and 1

normalized_grades = [(name, (grade - min_grade) / (max_grade - min_grade)) for name, grade in grades]

return normalized_grades

9. Given a family of 4, and given that there is an equal probability that a family member is born in a given season and each birth is independent, what is the probability that all 4 family members are born in different seasons?

If you would like to be a Data Scientist at DraftKings, your knowledge of probability concepts will be very important. This is because you’ll analyze sports data a lot, and you need to incorporate your probability knowledge to gain comprehensive insights.

How to Answer

First, you need to make sure to brush up on your understanding of the theory behind independent events. Then, explain that the probability of all four family members being born in different seasons can be calculated using the principle of independent events. Since each family member’s birth season is independent, the probability of each member being born in a different season can be multiplied together to find the overall probability.

Example

“To find the probability of all four family members being born in different seasons, we can use the concept of independent events in probability theory. Since each family member’s birth season is independent of the others, we multiply the probabilities of each member being born in a different season. Given that there are four seasons and each family member has an equal chance of being born in any season, the probability calculation becomes (¹⁄₄) * (³⁄₄) * (²⁄₄) * (¹⁄₄), resulting in ³⁄₃₂ or approximately 0.09375.”

10. What are the assumptions of linear regression?

This question assesses your understanding of linear regression, a fundamental statistical technique used in various aspects of sports analytics, such as predicting game outcomes, player performance, and betting odds adjustments.

How to Answer

To answer this question, you need to provide a concise explanation of the assumptions of linear regression by emphasizing their importance in ensuring the validity of the model’s results. Specifically, you need to briefly describe each assumption and its relevance to the linear regression model.

Example

“The assumptions of linear regression can be described as follows:

• Linearity: The relationship between the predictor variables and the response variable is linear.
• Independence: The residuals (the differences between observed and predicted values) are independent of each other.
• Homoscedasticity: The variance of the residuals is constant across all levels of the predictor variables.
• Normality of Residuals: The residuals are normally distributed.
• No Multicollinearity: The predictor variables are not highly correlated with each other.

Ensuring that the above assumptions are met in a linear regression model is very important to derive the correct interpretation from the model’s results.”

11. Which statistical models do you know that we can use as predictive models?

DraftKings prefers to use statistical models and predictive models in which the behavior can be explained compared to complex black box models like deep neural networks. Therefore, it is important that you, as an aspiring Data Scientist at this company, know different kinds of predictive models.

How to Answer

First, start by mentioning a variety of statistical models commonly used in predictive analytics that you know. Next, don’t forget to highlight their strengths and applications, their suitability for different types of data and prediction tasks, and mention how each model works. If it’s relevant to you, you can also discuss your experience with specific models and provide examples of how you used them in real-world projects.

Example

“From my previous role as a Data Scientist, I have experience in applying common statistical models used as predictive models, such as linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Linear regression is perfect for predicting continuous outcomes, while logistic regression is used for binary classification tasks. Decision trees and random forests are versatile models that can handle both regression and classification tasks, and they offer interpretability and flexibility. Support vector machines are powerful models for classification tasks with complex decision boundaries. Neural networks, especially deep learning models, are effective for handling large and complex datasets and extracting intricate patterns. In my previous projects, I’ve used a combination of these models to predict outcomes in various projects.”

12. Can you write a function that takes a list of dictionaries with a key and a list of integers and returns a dictionary with the standard deviation of each list?

When you’re applying for a Data Scientist position at DraftKings, you need to be able to implement algorithms in Python without relying on built-in functions like Numpy.

How to Answer

To answer the question, first, you need to make sure that you read the instructions carefully and understand the input that the function expects. As for the solution, you can iterate over each dictionary, extract the list of integers, and calculate the standard deviation using a common formula within the function.

Example

“In order for us to be able to compute the standard deviation correctly, first, we need a function that iterates over each dictionary in the input list and then calculates the mean and variance of the list of integers. Finally, we can compute the standard deviation using the square root of the variance. As an addition, we can also round the result to two decimal places and return a dictionary with the standard deviation for each list.”

def compute_deviation(list_numbers):

result = {}

for item in list_numbers:

key = item['key']

values = item['values']

mean = sum(values) / len(values)

variance = sum((x - mean) ** 2 for x in values) / len(values)

std_dev = variance ** 0.5

result[key] = round(std_dev, 2)

return result

13. How would you normally measure the performance of machine learning models, and how do you know when your model is performing well enough?

Your understanding of how to evaluate machine learning models is crucial if you want to become a Data Scientist, regardless of which industry you want to work in.

How to Answer

First, mention and explain common metrics and techniques used to measure the performance of machine learning models, such as accuracy, precision, recall, F1 score, ROC curve, and AUC. Next, discuss the importance of selecting appropriate evaluation metrics based on the specific goals and characteristics of the problem. Don’t forget to also highlight the importance of validating model performance on unseen data through techniques like cross-validation and monitoring model performance over time to ensure continued effectiveness.

Example

“Common techniques for measuring model performance that I know are accuracy, precision, recall, F1 score, ROC curve, and AUC. The choice of evaluation metrics depends on the nature of the problem and the desired outcome. For example, in sports betting scenarios, we may prioritize metrics like precision and recall to assess the model’s ability to identify winning outcomes accurately. It’s also essential to validate model performance on unseen data using techniques like cross-validation to ensure generalizability. After we deploy it in production, we need to continuously monitor the model performance over time to detect any degradation and ensure the model remains effective for ongoing predictions.”

14. Given a data frame that has two columns: day of the week and rainfall in inches, can you write a function to find the median amount of rainfall for the days on which it rained?

With this question in particular, people at DraftKings also want to assess your basic critical thinking and your problem-solving skills.

How to Answer

First, make sure that you understand the question and the task properly by reading the input and also the expected output. Then, write a function that takes a pandas DataFrame containing rainfall data as input. Within the function, you can filter the DataFrame to include only the days with nonzero rainfall, calculate the median rainfall for those days, and finally return the result.

Example

“To solve this problem, we can first filter the DataFrame to include only the days with nonzero rainfall, then calculate the median rainfall for those days using the median() method before we finally return the result. Below is an example of code implementation to solve the problem.”

import pandas as pd

def median_rainfall(df_rain):

# Filter the DataFrame to include only days with nonzero rainfall

rainy_days = df_rain[df_rain['Inches'] > 0]

# Calculate the median rainfall for rainy days

median_rain = rainy_days['Inches'].median()

return median_rain

15. Have you worked with statistical models or algorithms that would be particularly useful for sport analytics?

If you want to work as a Data Scientist at DraftKings, you need to understand statistical modeling to effectively analyze sports data and derive actionable insights, enhancing the company’s competitive edge in the sports industry.

How to Answer

Answer this question by highlighting your relevant experience with statistical models or algorithms commonly used in sports analytics. First, you need to provide specific examples of projects where you applied a specific model to analyze sports data or make predictions. Then, make sure that you also discuss the impact of your work on improving decision-making processes and driving business outcomes.

Example

“In my previous role, I had the opportunity to work with predictive modeling techniques such as logistic regression, random forests, and gradient boosting machines (GBM) to analyze sports data and forecast game outcomes. For example, I once developed a predictive model using random forests to predict the likelihood of a football team winning a match based on historical game data and player statistics. Additionally, I implemented time series analysis techniques to forecast player performance trends and then adjust betting odds dynamically. My contributions led to significant improvements in prediction accuracy and betting strategy optimization, ultimately driving positive business outcomes for my previous employer.”

16. Given a DataFrame with a single column called ‘var’, how can you write a function that calculates the t-value for the mean of ‘var’ against the null hypothesis?

If you would like to become a Data Scientist at a company like DraftKings, then knowledge of the concept of hypothesis testing or A/B testing is crucial since we need to continuously assess the significance of the product improvement on the overall user experience.

How to Answer

To answer the question, first, we need to understand the basic concept of t-tests. Then, we can write a Python function that takes two arguments: the null hypothesis mean and a DataFrame containing the data. Within the function, we can calculate the t-value for testing the mean of the data against the null hypothesis using the appropriate formula for a one-sample t-test.

Example

“To solve this problem, we can write a Python function to calculate the t-value for testing the mean of the ‘var’ column against the null hypothesis mean. First, we compute the sample mean, sample standard deviation, and sample size. Next, we can plug in the formula of one-sample t-test to calculate the t-value and return it at the end. Below is an example of code implementation for this.”

def t_score(mu0, df):

# Calculate the mean and standard deviation of the data

mean = df.mean()

std_dev = df.std(ddof=1) # Use Bessel's correction for sample standard deviation

# Calculate the sample size

n = len(df)

# Calculate the t-value

t_val = (mean - mu0) / (std_dev / (n ** 0.5))

return t_val

17. Can you talk about a time when you needed to think creatively to solve a problem with limited data or resources?

Data Scientists at DraftKings often encounter challenges that require innovative solutions to extract insights from incomplete or noisy data with limited resources. If you can demonstrate your creative thinking in overcoming such challenges, then your chances of getting hired will increase massively.

How to Answer

First, start by describing a specific situation where you encountered a problem with limited data or resources and had to think creatively to find a solution. Next, explain your approach to solving the problem, including any innovative techniques or methodologies you employed to overcome the constraints. Finally, you need to also highlight the impact of your creative solution on achieving the desired outcome or resolving the problem effectively.

Example

“In my previous role, I was tasked with predicting player performance in a padel tennis category where historical data was scarce and unreliable. To address this challenge, I adopted a creative approach by leveraging transfer learning techniques from similar sports categories, such as tennis, table tennis, and badminton, with more abundant data. I also fine-tuned pre-trained neural network models using the limited available data and augmented it with synthetic data generated through data augmentation techniques. This creative solution allowed us to build robust predictive models despite the limited data, ultimately enabling us to make accurate player performance predictions effectively.”

18. What do the AR and MA components of ARIMA models refer to, and how do you determine their order?

At DraftKings, it is expected that you’ll also work in the area of time-series analysis to forecast sports-related metrics, such as player performances, game outcomes, etc.

How to Answer

First, start by explaining the AR (AutoRegressive) and MA (Moving Average) components of ARIMA models. Next, mention how we can determine the order of the AR and MA using autocorrelation and partial autocorrelation plots. Don’t forget to mention that model selection criteria such as AIC and BIC would be useful in choosing the optimal order of ARIMA.

Example

“In ARIMA models, the AR (AutoRegressive) component represents the linear relationship between the current value of a time series and its past values, while the MA (Moving Average) component captures short-term, random fluctuations in the series. The order of the AR component specifies the number of lagged observations used, while the order of the MA component specifies the number of lagged residual errors used. To determine their order, we analyze autocorrelation and partial autocorrelation plots of the time series data. Significant spikes in the partial autocorrelation plot indicate the order of the AR component, while significant drops in the autocorrelation plot suggest the order of the MA component. We can use model selection criteria such as AIC or BIC to help us choose the optimal order of ARIMA models.”

19. What are the steps to perform a Monte Carlo simulation, and what is its advantage compared to traditional methods?

This question is asked in a Data Scientist interview at DraftsKing because Monte Carlo simulation is a powerful tool used in data analysis and decision-making, particularly in the sports betting industry.

How to Answer

First, start by outlining the steps required to perform a Monte Carlo simulation, such as defining the problem, specifying input parameters and their distributions, generating random samples, running simulations, and analyzing results. Next, mention the advantages of the Monte Carlo simulation over traditional methods, such as its ability to handle complex, stochastic processes and provide probabilistic insights into outcomes.

Example

“To perform a Monte Carlo simulation, we first define the problem we want to solve and identify the key variables and parameters involved. As an example, in order to predict the outcome of a football match, our parameters can be the weather, the form of the home and away teams, injury records, etc. Next, we specify probability distributions for these variables, generate random samples from these distributions, and use them as inputs for running simulations. After running a large number of simulations, we analyze the results to understand the range of possible outcomes and their probabilities. The advantage of the Monte Carlo simulation lies in its ability to handle complex, stochastic processes and uncertainty, allowing us to model real-world scenarios more accurately compared to traditional deterministic methods.”

20. Given n dice, each with m faces, how can you write a function to dump all of the possible combinations of dice rolls?

This question assesses your statistics and programming skills at once. When you’re applying for a Data Scientist position at DraftKings, you need to be able to think critically and then put your idea into a working code.

How to Answer

First, make sure that you understand the question correctly. Then, provide your explanation of how we can solve this combination problem mathematically. Finally, demonstrate your understanding of recursion and its application in generating combinations in code.

Example

“To solve this problem, we can use the concept of recursion to generate all possible combinations of dice rolls. To implement this, we will write a helper function, which is a recursive function that takes the number of dice left to roll (n), the number of faces on each die (m), and the current combination of rolls (current) as arguments. At each recursive call, it iterates over all possible outcomes of rolling a single die (from 1 to m), appending the outcome to the current combination and recursively calling itself with n - 1 dice left to roll. When n becomes 0, meaning all dice have been rolled, it appends the current combination to the result list.

Below is an example of code implementation for this problem:”

def combinational_dice_rolls(n, m):

def helper(n, m, current=[]):

if n == 0:

result.append(tuple(current))

else:

for i in range(1, m + 1):

helper(n - 1, m, current + [i])

result = []

helper(n, m)

return result

In this section, we’ll explore several tips to effectively prepare the interview process to give you that competitive edge over your competitors.

Understand the Company and Industry

Before diving into technical preparations, it’s crucial to gain a comprehensive understanding of DraftKings by familiarizing yourself with the domain of the company, which is the sports entertainment industry.

You can learn everything about their products, services, and business model on their website, as it provides different kinds of materials that you can use to get to know the company better. On the company website, you can listen to their podcast, read their engineering blog, watch their YouTube videos, etc.

Master Data Science Fundamentals

DraftKings values candidates who possess a strong foundation in data science principles. As you can see from the questions in the previous section, you will likely encounter questions related to data preprocessing, statistics, probability, and machine learning.

Therefore, it is important that you brush up on the core concepts of data science, such as hypothesis testing, regression analysis, classification algorithms, and exploratory data analysis. If you’re looking for detailed learning materials regarding the fundamentals of data science that we have mentioned above, then you can check out our Data Science Learning Path, Statistics Learning Path, and ML Learning Path.

To complement the theory part, you need to also practice implementing different kinds of machine learning algorithms and data structures in popular programming languages like Python or R. If you would like to practice your algorithm as well as your programming skills, then you can head up to our question banks and you can also take challenges to test your skills against your peers.

Showcase Domain Knowledge

Given DraftKings’ focus on sports entertainment, showing the fact that you also have a passion for sports would be a big bonus point for you. Your chance of success would massively increase if you can show evidence that you have combined your passion for sport with your knowledge in data science.

Therefore, it’s important that you prepare a small project that showcases your interest in sports analytics, fantasy sports, betting markets, or player performance analysis. The project that you have prepared would also be a very good talking point during the interview process.

Here at Interview Query, we have take-home challenges where you can work on different kinds of projects, and then you can practice your ability to gather the findings and present it in a structured way with a notebook.

Practice Effective Communication

In addition to technical skills, effective communication is essential for success in a Data Scientist role at DraftKings.

Therefore, it is necessary to practice articulating your thoughts in a clear and concise way, especially when explaining complex analytical concepts or presenting your findings.

In order to help you practice your communication skills, we have a mock interview service on our site in which you can practice articulating your thoughts with other data professionals or fellow data enthusiasts who are also preparing for the interview. We also offer a coaching service where you can get personalized guidance for your interview preparation.

Those are several tips that you can apply straight away. However, if you’re looking for more detailed tips for your Data Scientist interview preparation, then you can check out our in-depth tips dedicated to this.

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

How much do Data Scientists at DraftKings make in a year?

The base pay for a Data Scientist at DraftKings is currently unknown, as we still need to gather more data points on that. However, you can check the average base salary and the average total compensation for a Data Scientist in general on Interview Query’s Data Scientist Salary page.

Where can I read more about people’s interview experiences for a Data Scientist position at DraftKings here on Interview Query?

We do not currently have a section on interview experience for the Data Scientist position at DraftKings at the moment. However, you can read about other people’s interview experiences at other companies for Data Scientists, as well as other data-related positions, in our interview experiences section.

Does Interview Query have job postings for DraftKings’ Data Scientist position?

We do not directly list job postings for a specific company and position, including a Data Scientist position at DraftKings. If you wish to check the most up to-date available Data Scientist or other data-related positions, we would recommend you to directly go to their official career website.

If you wish, you can also consider finding new opportunities for a Data Scientist position on our Jobs Board. It’s updated with the most recent job postings for a Data Scientist and other data-related positions from some of the most famous companies around the world.

In this article, you’ve seen typical interview questions for a Data Scientist position at DraftKings. If you need more materials to test your knowledge, then don’t worry, as we also have a general article about data science interviews dedicated to this. There, you can find lots of general data science interview questions and not specific to a particular company.

If you’re also interested in knowing the interview process for other data-related positions at DraftKings, feel free to check them out on our site, as we have covered their Business Analyst, Data Engineer, Product Analyst, Data Analyst, and Software Engineer Interview Guides for you.

Preparing for a Data Scientist interview at DraftKings can be challenging as you need to cover several typical data science questions that will test your technical skills, business knowledge, and interpersonal skills. Therefore, we also have several learning materials on our site that will help you to sharpen your skills in all of those areas, such as examples of technical questions regarding data science projects, data science case studies, or Python, as well as behavioral questions.

We hope that this article is helpful to you in your preparation for the interview process for the Data Scientist position at DraftKings. If you have questions or need help, don’t hesitate to contact us, and be sure to check out the services that we offer on our site.