All You Need to Know About the Meta DS Technical Skills Interview

1. Data Analysis Skills

As a Meta data scientist, you'll be at the deep end of some seriously robust datasets. Fluency in SQL is a must, and the interview will likely focus heavily on SQL-related problems. So, get ready to showcase your SQL prowess by writing queries on a whiteboard or online equivalent. They'll take you through scenarios where you analyze and interpret data, proving you can turn it into actionable insights. 

2. Programming Proficiency

Meta doesn't mess around when it comes to coding skills—expect a rigorous examination here. You are typically given an open-ended product problem and asked to solve it using code. It's not just about correctness; they want your code to be crystal clear and super efficient. So, along with SQL, brush up on your data structures and algorithms – they will throw those into the mix. You need to show you can translate complex theoretical solutions into functional, real-life code. 

3. Effective Communication

Beyond coding, Meta values your communication skills. Can you structure and articulate solutions clearly? This involves explaining your thought process in a way that even non-technical stakeholders can comprehend.

4. Setting Goals & Success Metrics

The interview also gauges your ability to set goals and success metrics aligning with broader business objectives. Basically, they want to see if you can connect your solutions to actual business impact. How do your technical skills make a difference in the big picture?

Let's breakdown the types of questions you can expect in this interview:

1. Open-Ended Problems:

Firstly, you'll encounter open-ended problems, where your ability to structure and articulate solutions based on available data will be tested. So, for example, you might be asked: "Given a dataset on user engagement, how would you structure a solution to improve user interaction on our platform?”

1. Hands-on Coding Questions

Next, they'll test your ability to code executable solutions. You'll be asked to write code that solves a specific problem. An example could be: "Code a solution to analyze customer behavior data and identify patterns that could inform marketing strategies."

1. Handling Edge Cases:

Thirdly, questions revolving around identifying and addressing edge cases will come up. You will encounter follow-up enquiries to your solutions like: "Consider a scenario where user behavior is drastically different on weekends. How would you modify your analysis to account for this edge case?”

1. Adapting to New Data

This set of questions tests your flexibility when faced with new information. Can you modify existing code for improved results? An example could be: "You're analyzing sales data, and suddenly, a new dataset with customer feedback on product preferences becomes available. How would you adapt your existing analysis code to incorporate this new information and enhance your insights?" 

1. Coding Efficiency

These questions gauge your ability to optimize code for improved performance. An example could be: "Optimize a given algorithm for sentiment analysis. Explain your modifications and how they enhance the efficiency of the code." Here, you're expected to not only produce a working solution but also to make it more efficient.

• Given user activity data, identify the users with the most consistent daily visits over the past week.
• Given a list of pages and their statuses, determine the percentage of pages that are currently active.
• In a scenario with forums and replies instead of posts and comments, how would you calculate the average number of discussions happening in the replies for each forum topic?
• With two tables indicating interactions and posts, calculate the total number of shares on friend posts that occurred yesterday.
• Elaborate on the differences between inner join, outer join, and union in the context of combining data from two tables.
• Suppose you have an algorithm that categorizes emails as spam. How would you assess the effectiveness of this spam detection algorithm?
• In a list of integers, identify groups of consecutive numbers whose sum is divisible by a specific value.
• Given a dataset with individuals and their belongings, determine which items are shared most frequently among different people.
• In a user activity log, figure out how to identify the initial log-on date for each user on a platform.
• How would you implement a function to reverse the order of words in a given sentence?

1. Brush up on your data structures, algorithms, and be ready for those open-ended product problems. Practice writing queries, especially on a whiteboard or an online platform. You should especially be ready for scenarios requiring data interpretation and actionable insights.
2. Respond to open-ended problems using a storytelling narrative. Start by clearly stating the problem at hand. For example, if the goal is to enhance interaction on the X platform, articulate this as the central focus. Systematically go through each step of your solution. Discuss factors influencing user engagement, such as content relevance and user interface. It's not just about the "what" but also the "why" behind your choices.
3. Acknowledge any new information or constraints that might be introduced during the discussion. Demonstrate your ability to seamlessly integrate these insights into your solution—show flexibility in adapting to evolving scenarios.
4. When tackling edge cases, explicitly mention your consideration for outliers. Explicitly discuss how your solution not only addresses general scenarios but also considers nuanced situations—such that your approach is robust enough to handle outliers or less common scenarios. Also, be prepared to discuss how your technical solutions contribute to tangible business impact. This will help you articulate your experiences during the interview and demonstrate the practical application of your skills.
5. Use the programming language you're most comfortable with. The focus is on your problem-solving approach, and the language should facilitate clear communication.
6. While it's acceptable to skip minor syntax details, ensure that your solution is explained clearly. Interviewers want to see your actual problem-solving steps, not just high-level ideas.
7. If you're using less-common functions, provide a quick context to ensure the interviewer follows your logic. This helps in maintaining a smooth and clear communication flow.
8. Final Tip: Practice extensively. Hit platforms like LeetCode and HackerRank and practice a variety of coding exercises to build comfort and familiarity with different scenarios. Practice answering questions related to SQL, coding problems, and effective