```markdown WhatsApp Inc., a subsidiary of Meta Platforms, is a groundbreaking communication service known for its reliability, simplicity, and privacy-centric approach that connects over 2 billion users globally. As a Data Scientist at WhatsApp, you will be integral to deriving key insights from the vast amount of data generated daily, helping shape the product and its functionalities.

This position demands expertise in data science, including statistical analysis, machine learning, and data mining techniques, alongside strong programming skills in languages such as Python and SQL. You will be expected to collaborate with various teams to drive data-driven decision-making processes, ensuring the continuous enhancement of user experience and overall platform performance.

To help you succeed in your interview, our guide on Interview Query will outline the interview process, common questions, and strategic tips tailored for aspiring WhatsApp Data Scientists. Dive in and prepare to make an impact with your data expertise! ```

Submitting Your Application

The first step to becoming a data scientist at WhatsApp Inc. is to submit a compelling application that showcases your technical skills and articulates your enthusiasm for the role. Whether you were contacted by a WhatsApp recruiter or applied on your own, it is important to carefully review the job description and tailor your CV to meet the specific requirements.

Tailoring your CV may include identifying relevant keywords that the hiring manager might search for and crafting a targeted cover letter. Additionally, be sure to highlight pertinent skills and experience.

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, a recruiter from WhatsApp’s Talent Acquisition Team will reach out to verify key details about your experience and skills. During this initial screening, behavioral questions may be asked to assess your fit for the team and company culture.

Sometimes, the hiring manager may also be present during this call to answer any questions you might have about the role and WhatsApp as a company. They might engage in introductory technical and behavioral discussions.

The recruiter call usually lasts about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will lead to an invitation for the technical screening round. For WhatsApp data scientist roles, the technical screening typically takes place virtually via video conference and screen sharing. Questions in this 1-hour interview may revolve around WhatsApp’s data architecture, data analysis, and SQL queries.

Expect take-home assignments related to data analytics, product metrics, and data visualization. You may also be tested on hypothesis testing, probability distributions, and foundational machine learning concepts.

Based on the job's seniority, you may also be given case studies or real-world scenario problems to solve.

Onsite Interview Rounds

Following a second recruiter call that outlines the next steps, you'll be invited for onsite interview rounds. Several interviews may be scheduled during your day at WhatsApp’s office, depending on the role. These rounds will evaluate your technical skills, including programming and machine learning capabilities, against other final candidates.

If you were given take-home assignments, you might also have to present your findings during the onsite interview for the data scientist role at WhatsApp.

Quick Tips For WhatsApp Inc. Data Scientist Interviews

• Master Data Fundamentals: WhatsApp's data science interviews focus heavily on data analytics and machine learning. Be well-versed in SQL, Python, and data analysis techniques.
• Understand WhatsApp’s Products: Prepare yourself for situational questions related to WhatsApp’s features and services. Understand how data science can enhance user experience and product efficiency.
• Highlight Collaboration: WhatsApp values a collaborative and communicative work culture. Practice answering behavioral questions that demonstrate your ability to work well in teams and adapt to a dynamic work environment.

Typically, interviews at Whatsapp Inc. vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you set up this test?

• Would you suspect anything unusual if an A/B test with 20 variants shows one significant result? Your manager ran an A/B test with 20 different variants and found one significant result. Would you think there was anything fishy about the results?

• Why might the average number of comments per user decrease despite user growth in a new city? A social media company launched in a new city and saw a slow decrease in the average number of comments per user from January to March, despite consistent user growth. What are some reasons for this decrease, and what metrics would you look into?

• What metrics would you use to determine the value of each marketing channel for a B2B analytics company? Given all the different marketing channels and their respective costs at a company called Mode, which sells B2B analytics dashboards, what metrics would you use to determine the value of each marketing channel?

• How would you locate a mouse in a 4x4 grid using the fewest number of scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can "scan" subsets of cells to know if the mouse is within that subset but not its exact location. How would you figure out where the mouse is using the fewest number of scans?

• Create a function find_bigrams to return a list of all bigrams in a sentence. Write a function called find_bigrams that takes a sentence or paragraph of strings and returns a list of all its bigrams in order. A bigram is a pair of consecutive words.

• Write a query to get the last transaction for each day from a table of bank transactions. Given a table of bank transactions with columns id, transaction_value, and created_at representing the date and time for each transaction, write a query to get the last transaction for each day. The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.

• Create a function find_change to find the minimum number of coins for a given amount. Write a function find_change to find the minimum number of coins that make up the given amount of change cents. Assume we only have coins of value 1, 5, 10, and 25 cents.

• Write a function to simulate drawing balls from a jar based on their counts. Write a function to simulate drawing balls from a jar. 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.

• Create a function calculate_rmse to compute the root mean squared error. Write a function calculate_rmse to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

• What is the expected number of good ads rated by different types of raters?
• Suppose we have 100 raters each rating one ad independently. What's the expected number of good ads?
• Now suppose we have 1 rater rating 100 ads. What's the expected number of good ads?

• Suppose we have 1 ad, rated as bad. What's the probability the rater was lazy?

• Write a function to simulate coin tosses with a given probability of heads. Create a function that takes the number of tosses and the probability of heads as input and returns a list of randomly generated results ('H' for heads, 'T' for tails).

Example 1: Input: tosses = 5, probability_of_heads = 0.6 Output: coin_toss(tosses, probability_of_heads) -> ['H', 'T', 'H', 'H', 'T']

Example 2: Input: tosses = 3, probability_of_heads = 0.2 Output: coin_toss(tosses, probability_of_heads) -> ['T', 'T', 'T']

• Write a function to calculate the sample variance of a list of integers. Create a function that takes a list of integers and returns the sample variance, rounded to 2 decimal places.

Example: Input: test_list = [6, 7, 3, 9, 10, 15] Output: get_variance(test_list) -> 13.89

• What is the probability of rolling at least one 3 with dice?
• What's the probability of rolling at least one 3 with 2 dice?

• What's the probability of rolling at least one 3 given (N) dice?

• What is the probability of finding an item on Amazon's website given its availability in warehouses? Given that the probability of item X being available at warehouse A is 0.6 and at warehouse B is 0.8, what is the probability that item X would be found on Amazon's website?

• What kind of model did the co-worker develop? Your co-worker developed a model that takes customer inputs and returns if a loan should be given or not. What kind of model is this?

• How would you measure the difference between two credit risk models? Given that personal loans are monthly installments of payments, how would you measure the difference between two credit risk models within a timeframe?

• What metrics would you track to measure the success of a new credit risk model? What metrics would you track to measure the success of a new model predicting loan defaults?

• What metrics would you use to track the accuracy and validity of a spam classifier? You have built a V1 of a spam classifier for emails. What metrics would you use to track its accuracy and validity?

• What are the key differences between classification models and regression models? Explain the key differences between classification models and regression models.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? Provide an example of the tradeoffs between the two.

• What happens when you run logistic regression on perfectly linearly separable data? You are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

What is the average salary for a Data Scientist at Whatsapp Inc.?

Q: What is the interview process for a Data Scientist position at Whatsapp Inc. like?

A: The interview process at Whatsapp Inc. typically includes a phone screen with HR, followed by multiple technical interviews. These technical rounds assess your expertise in data analysis, machine learning, and coding. Finally, you'll have an onsite interview, which may include problem-solving exercises, coding challenges, and discussions on past projects.

Q: What types of questions can I expect in the interviews?

A: Expect questions that test your knowledge in statistics, machine learning, and data manipulation. You might also encounter coding challenges that require you to write and debug code. Furthermore, be prepared for scenario-based questions that assess your ability to solve real-world data problems.

Q: What key skills are required for a Data Scientist at Whatsapp Inc.?

A: Essential skills include proficiency in programming languages like Python or R, experience with data visualization tools, and a strong grasp of statistical methods and machine learning algorithms. Additionally, excellent problem-solving abilities and familiarity with large datasets are crucial.

Q: What is the work environment like at Whatsapp Inc.?

A: Whatsapp Inc. fosters an innovative and collaborative environment. Data Scientists work across teams to integrate data-driven decisions into product development and strategy. The culture encourages continuous learning and values contributions that drive meaningful user experiences.

Q: How can I best prepare for an interview at Whatsapp Inc.?

A: To prepare, you should hone your technical skills, especially in coding and machine learning. Research the company’s data products and recent projects. Using Interview Query to practice common Data Scientist interview questions can be particularly beneficial. This will help you get accustomed to the types of problems you may face during the interview process.

Prepare yourself for an exciting career at WhatsApp Inc. as a Data Scientist by taking advantage of the insights and resources we provide. For a more comprehensive understanding of the company, check out our main WhatsApp Inc. Interview Guide, where we've covered many potential interview questions. Additionally, explore our detailed guides for other roles, such as software engineer and data analyst, to get a clear picture of WhatsApp’s interview processes across various positions.

At Interview Query, we empower you with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every interview challenge at WhatsApp Inc.

Explore all our company interview guides to enhance your preparation, and if you have any questions, feel free to reach out to us.

Good luck with your interview!