Saturday, July 13, 2024

Facebook Data Science Manager Interview

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Top Advanced Data Scientist Interview Questions

The Facebook Data Science Interview Questions
Whats the difference between support a vector machine and logistic regression? Please provide examples of situations where you would choose to use one rather than the other.
If removing missing values from a dataset causes bias, what would you do?
When looking at a products health, engagement, or growth, what metrics would you assess?
When trying to address or solve business problems related to our product, what metrics would you assess?
How do you judge product performance?
How do you know if a new observation is an outlier?
What is the bias-variance trade-off?
What is your method for randomly selecting a sample from a product user population?
What is your process for data wrangling and cleaning before applying machine learning algorithms?
How do you differentiate between good and bad data visualization?


Most Common Data Science Interview Questions

While there are no guarantees, here are 10 interview questions youll likely encounter:

  • What is the difference between supervised and unsupervised learning?
  • What is the difference between data science and data analytics?
  • Explain the steps in making a decision tree. How would you create a decision tree?
  • Youre given a data set thats missing more than 30% of the values. How do you deal with that?
  • How do you/should you maintain a deployed model?
  • How is data science different from other forms of programming?
  • How often do you/should you update algorithms?
  • What is the goal of A/B testing?
  • What are the differences between overfitting and underfitting, and how do you combat them?
  • What do you prefer using for text analysis?
  • Bellassai also notes that while you should give your best answer to the question, there is no right answer. Remember that there is no perfect solution. A particular approach isnt necessarily the best just because youve used it before.

    How To Pass Leadership Screening

    The main objective for this round is for the interviewer to know you as a manager. Prepare answers to describe:

    • What is your management style?
    • How do you manage your team?
    • How to you manage your top performers?
    • How do you manage bottom performers?
    • How do you manage average performer?
    • How do you hire in your team?
    • Have you fired anyone?
    • What is the most difficult thing you had to do in your managerial career?

    The key to clearing this section is to give the interviewer what he/she is looking for. For instance, a very good answer for the most difficult thing could be firing someone. Now, it is very likely that you never had to fire anyone. In that case, see if you can think of some other situation. If not, then make one. Trust me. If you dont have a relevant scenario, fabricate one that shows what one should do under a given scenario. Keep in mind that the interviewer is looking for a challenging situation and how you deal with it in a systematic or structured way without letting emotions control your behavior.

    Do take time and think about answers for all the above questions. This will give you enough material to use in your actual interview. In case you dont have much experience in this area or you are having trouble articulating the answer, just google these questions and you will come across enough material.

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    How To Pass Tech Screening

    There is no short-cut to this round, you have to prepare. The technical screening round will have three sections:

    To clear data modeling, you must know the below concepts at the minimum:

    It would be a great idea to practice how to define a dimension and a fact table for any generic scenario. For example, in the case of a ride sharing app, we can think of customer and driver as dimensions and ride details as fact tables. If you can define the structure of these tables, you will pass the data modeling section. Make sure that you explain how you defined the table structure.

    For the next section of SQL, the questions will be based on the data model you just created. To clear this round, you must know below:

    • Filter conditions
    • Aggregate functions using Group By & Having clauses

    The third section will be the coding round where you will be asked to write code using any language of your choice. This round is very basic and covers basic data structure elements. You can easily cover this round if you know below:

    • Define functions

    The best advice for this section is to go through all questions mentioned on Glassdoor. If you do those for practice, you can easily clear this section.

    Q3how Do You Know Whether A Specific Product Is Engaging As Intended


    This question also relies on important measurement and analytics skills. This question is multifaceted since candidates must demonstrate knowledge of the following:

  • Consumer pain points for the industry at hand.
  • Variables that constitute success for a product, such as clicks, hours spent on a software product, and the number of purchases, among others.
  • Candidates must provide multiple metrics to measure success. Case studies or past successes from previous positions are references that you must bring up when answering this question. The most impressive candidates explain prior products they launched. In doing so, speak about the importance of ensuring that the target audience uses a product as a company predicts.

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    Q: How Do You Investigate Reductions In Products Or Subscriptions Sold

    In this scenario, the interviewer wants the candidate to demonstrate expertise in analyzing sales metrics and KPIs. They want to know that a new data science product manager understands how to:

    • Analyze price points against similar companies
    • Re-examine core customer experience differences between various products
    • Investigate developing or evolving customer needs and desires to ensure that product solutions align with those desires

    They also want the candidate to demonstrate a deep understanding of important product-related success metrics.

    Credits: ProductPlan

    These include a products conversion rate as well as possible revenue and profits generated from the product.Interviewers also want candidates to show how they determine the root causes of profit reductions. Once you provide the right answer, candidates show that they solve bleeding cash problems.Candidates must answer this question in detail by showcasing their ability to analyze KPIs.

    Candidates stand out when they provide specific examples of what metrics they look at and how those metrics relate to profit losses.

    Meta Data Engineering Manager Interview Questions

    • – Ar Rifa’ wa al Mintaqah al Jan
    • – Al Mintaqah al Gharbiyah
    • Bosnia and Herzegovina – All Cities
    • – Federation of Bosnia and Herze
    • – Bosnia Serb Republic
    • – San Andrés and Providence and
    • Papua New Guinea – All Cities
    • – Baladiyat az¸ Z¸aayin
    • United Arab Emirates – All Cities


    I applied through a recruiter. The process took 5 weeks. I interviewed at Meta


    it was a standard interview process with following steps: 1. Tech phone screen2. People leadership phone screen3. 5 on-sites 3 people, 2 techPrepare the questions shared here on Glassdoor and you should have a good sample size on what’s commonly asked.

    Anonymous Interview Candidate in London, England


    I applied through a recruiter. The process took 3 months. I interviewed at Meta in Sep 2021

    InterviewInterview Questions

    • A lot of rounds covering all the aspects of technical manager + people manager + organizational leader that I liked as they were curious to know all the aspects of your professional ability and achievements.

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    Q: How Do You Measure A Products Success Within A Specific Target Market

    This question narrows the data science product manager candidates analytical focus to a products performance within target markets or demographics.Top candidates prove their worth in developing and launching successful data science products by showing their measurement skills. By demonstrating measurement skills, they indicate that they know how to predict what consumers appreciate or desire in future products.Be sure to do the following when answering this question:

  • Mention a variety of analytics strategies, such as offering surveys, examining month-over-month or year-over-year profits or revenue for a specific product
  • 2. Examine how a specific product performs relative to competing products within the same company or against similar products found at other organizations

    Common Data Science Interview Questions

    Facebook / Meta SQL Interview Question for Data Scientists and Data Analysts (StrataScratch 10064)

    Getting ready for data science interview questions is, in some respects, no different than preparing for an interview in any other industry. Youll research the company, prepare answers to common interview questions, and review your portfolio to use during the interview.

    However, preparing for a data science interview involves more than preparing for questions like Why do you think you are qualified for this position? Data scientist interviews include a lot of technical topics. And while you might be comfortable talking about your abilities, can you explain them in a way that makes sense to the hiring manager?

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    Operational And Situational Questions

    • What data would you compile to forecast our companys growth for next year?
    • How would you organize data into spreadsheets to track performance metrics for each department? What kinds of reports would you produce?
    • What models would you use to forecast monthly sales revenues based on historical data?
    • How would you explain to senior managers how a big data model works, using simple language?

    Q: How Do You Investigate Increased Profits Or Users For A Product

    This question analyzes a candidates ability to discover the reasons for profit improvements rather than reductions. Data science product managers answer by:

    • Examining and analyzing market factors, including competitors or industry-wide trends
    • Breaking down full analyses of consumer behavior patterns
    • Showing why specific products answer pain points better than others
    • Highlighting the most important parts of company products

    Candidates must answer this question by demonstrating that they know how to dive deep into important key performance indicators for specific products. In doing so, you need to show that you understand what makes products sell.

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    Q: How Do You Improve A Product At Your Current Company

    This question requires candidates to draw on current or past experience. Data science product managers must answer by explaining a situation in which they saw something improvable at their current organization.Be sure to do the following:

  • Provide an example of a product that needs some work in terms of customer retention, marketing, or functionality.
  • Explain how you improve that product. Connect the improvements to the design principles mentioned in the above interview question.
  • Break down why your improvements are feasible for the company.
  • Explain how the improvements yield concrete, measurable results for the company.
  • Common Behavioral Data Science Interview Questions

    Artificial intelligence meets the C

    With behavioral interview questions, employers are looking for specific situations that showcase certain skills. The interviewer wants to understand how you dealt with situations in the past, what you learned, and what you are able to bring to their company.

    Examples of behavioral questions in a data science interview include:

    Question: Do you recall a situation when you had to clean and organize a big data set?

    Answer: Studies have shown that Data Scientists spend most of their time on data preparation, as opposed to data mining or modeling.

    If you have any experience as a Data Scientist, it is almost certain that you have experience cleaning and organizing a big data set.

    Data cleaning is also one of the most important steps for any company. So you should take the hiring manager through the process you follow in data preparation:

    • Removing duplicate observations
    • Data validation

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    How Do You Prepare For A Data Science Interview

    What topics do Data Science interviews cover?

    Data Science interviews cover Probability, Statistics, Machine Learning, SQL& Database Design, Python Coding Questions, & Product Sense. That’s why Ace the Data Science Interview has a chapter dedicated to each topic – it’s everything you need for Data Science, Data Analyst, and Machine Learning interviews.

    Where can I get Data Science interview questions?

    Ace the Data Science Interview has 201 questions from real Data Science interviews, with full solutions for each problem. These interview questions come from companies like Facebook, Google, Amazon, Microsoft, Netflix, Stripe, Uber, Two Sigma and Citadel.

    “helped me land my dream job” âAdvitya, ML @ Microsoft

  • Learn how to write cold emails in Chapter 3
  • Learn how to ace behavioral interviews in Chapter 4
  • “Cracking the Coding Interview but for DS & ML!â â Jack Morris, AI @ Google

    Additional Personal Data Scientist Interview Questions

    Please tell me about yourself.
    What are your best qualities professionally? What are your areas of weakness?
    Is there one Data Scientist you admire most?
    What inspired your interest in data science?
    What unique skills or characteristics do you bring that would help the team?
    What made you decide to leave your last job?
    What level of compensation are you expecting from this job?
    Do you prefer to work alone or as a part of a team of Data Scientists?
    Where do you see your career in five years?
    Whats your approach for handling stress on the job?
    How do you find motivation?
    Whats your method for measuring success?
    How would you describe your ideal work environment?
    What are your passions or hobbies outside of data science?

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    Additional Technical Data Scientist Technical Interview Questions

    Have you worked on a data science project that required a substantial programming component? What did you take away from the experience?
    Describe how to effectively represent data with five dimensions.
    You need to generate a predictive model using multiple regression. Whats your process for validating this model?
    How do you ensure that the changes youre making to an algorithm are an improvement?
    Please provide your method for handling an imbalanced data set thats being used for prediction .
    Whats your approach to validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?
    You have two different models of comparable computational performance and accuracy. Please explain how you decide which to choose for production and why.
    You are given a data set consisting of variables with a substantial portion missing values. Whats your approach?

    How To Crack Facebooks Data Engineering Manager Interview

    Facebook Product Investigation Mock Interview: Part 1 (Fill Rate)

    Data Engineering manager interviews at Meta are truly challenging. They are thoughtfully structured with clear objectives for each round.

    Three years ago, I was being interviewed for Data Engineering Manager and I didnt make it. I got a chance to apply for the same role again this year, and I nailed it. This article is to share my preparation and personal experience.

    Note: No questions are shared because of Non-Disclosure Agreement with the company.

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    Product Design Data Science Product Manager Interview Questions

    Interviewers for data science product manager positions often ask questions about product design. Designing a successful product and ensuring that it addresses consumer pain points is a major part of these jobs. Interviewers look for candidates who show the following:

    • An understanding of the companys products and target market.
    • A general sense of what it takes to develop a product from start to finish.
    • An ability to define and explain relevant metrics to the products solution.
    • A keen sense of how to measure product impact by crosschecking KPIs and key performance indicators

    As a candidate, you must provide precise responses that capture the kind of answers that interviewers want. Be sure to show off your knowledge of the companys products and target audience to impress the interviewer.

    Common Situational Data Science Interview Questions

    Leadership and communication are two valuable skills for Data Scientists. Employers value job candidates who can show initiative, share their expertise with team members, and communicate data science objectives and strategies.

    Here are some examples of leadership and communication data science interview questions:

    Question: What do you like about working on a multi-disciplinary team?

    Answer: A Data Scientist collaborates with a wide variety of people in technical and non-technical roles. It is not uncommon for a Data Scientist to work with developers, designers, product specialists, data analysts, sales and marketing teams, and top-level executives, not to mention clients.

    In your answer to this question, you need to illustrate that youre a team player who relishes the opportunity to meet and collaborate with people across an organization.

    Choose an example of a situation where you reported to the highest-level people in a company to show not only that you are comfortable communicating with anyone, but also to show how valuable your data-driven insights have been in the past.

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    Q: How Do You Investigate Fewer Users/buyers Of A Product

    The interviewer wants the candidate to dive deeper and demonstrate an understanding of customer behavior patterns and how they relate to products.The goal here for candidates is two-fold:

  • Showthat you know how to investigate the reasons why people stop purchasing or using products or services.
  • Demonstrate various information-gathering techniques. These include starting surveys and checking industry-wide trends to figure out whether this is industry-specific or something happening only in your specific company.
  • Provide specific examples or hypothetical scenarios that show that you have a deep understanding of customer consumption patterns. Explain why customers drop products they no longer use.

    Emphasize possible reasons such as:

    • Products no longer solve customer pain points
    • Prices are too high
    • Brand identity does not connect with the target audience

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