2nd Watch is a leading cloud consultancy that specializes in managing and optimizing cloud infrastructure across major platforms such as AWS, Azure, and Google Cloud. The company is renowned for its expertise in cloud security, innovation, and strategic data insights.

As a Data Scientist at 2nd Watch, you'll be at the forefront of leveraging data to drive cloud-based solutions and innovations. This role demands a strong foundation in machine learning, statistical analysis, and data modeling, alongside proficiency in programming languages like Python and R. You will be responsible for deriving actionable insights from complex data sets to help clients optimize their cloud strategies.

If you're considering this dynamic role, Interview Query is here to guide you through the interview process. We provide sample questions, detailed interview formats, and tips to help you succeed. Let’s dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining 2nd Watch as a Data Scientist. Whether you were contacted by a 2nd Watch recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the 2nd Watch Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the 2nd Watch Data Scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the 2nd Watch Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around data systems, ETL pipelines, and SQL queries.

In the case of data scientist roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the 2nd Watch office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Scientist role at 2nd Watch.

Quick Tips For 2nd Watch Data Scientist Interviews

• Understand ETL Processes: 2nd Watch focuses on cloud services and data management. Be prepared to discuss your experience with ETL processes and data pipelines in detail.
• Brush Up on Machine Learning Algorithms: Ensure that you are comfortable with various machine learning algorithms and how to apply them to real-world problems.
• Behavioral Questions: Emphasize your ability to work in a team and manage projects. 2nd Watch values collaborative and efficient team players.

Typically, interviews at 2Nd Watch vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to find the maximum number in a list of integers. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

• Create a function convert_to_bst to convert a sorted list into a balanced binary tree. Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should have a height difference of at most one between the left and right subtrees of all nodes.

• Write a function to simulate drawing balls from a jar. 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.

• Develop a function can_shift to check if one string can be shifted to become another. Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

• What are the drawbacks of having student test scores organized in the given layouts? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in "messy" datasets.

• How would you locate a mouse in a 4x4 grid using the fewest scans? You have a 4x4 grid with a mouse trapped in one cell. You can scan subsets of cells to know if the mouse is within that subset. Describe a strategy to find the mouse using the fewest number of scans.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers. Describe how you would decide which Dashers do these deliveries and whether the criteria for selection would be the same for both cities.

• What factors could bias Jetco's boarding time study results? Jetco, a new airline, had a study showing it has the fastest average boarding times. Identify factors that could have biased this result and what you would investigate further.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? A B2B SAAS company wants to test different subscription pricing levels. Describe how you would design a two-week-long A/B test to evaluate a pricing increase and determine if it is a good business decision.

• How much should we budget for a $5 coupon initiative in a ride-sharing app? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider and services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of both or only one rider getting a coupon? A driver using the app picks up two passengers. Determine the probability of both riders getting the coupon and the probability that only one of them will get the coupon.

• What is a confidence interval for a statistic and why is it useful? Explain what a confidence interval is, why it is useful to know the confidence interval for a statistic, and how to calculate it.

• What is the probability that item X would be found on Amazon's website? Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that item X is available at warehouse A (0.6) and warehouse B (0.8), calculate the probability that item X would be found on Amazon's website.

• Is a coin that comes up tails 8 times out of 10 fair? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if this coin is fair.

• What are time series models and why are they needed? Describe what time series models are and explain why they are necessary when less complicated regression models are available.

• How would you justify the complexity of building a neural network model and explain predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of the model and explain its predictions to non-technical stakeholders?

• How would you evaluate and deploy a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate if a decision tree is the correct model, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain how random forest generates its forest of trees. Additionally, why would you choose random forest over other algorithms like logistic regression?

• How would you explain linear regression to a child, a college student, and a mathematician? Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to each audience's understanding level.

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

What is the average salary for a Data Scientist at 2Nd Watch?

Q: What is the interview process like for a Data Scientist position at 2nd Watch?
A: The 2nd Watch interview process typically includes an initial recruiter screen, followed by technical assessments and multiple rounds of interviews. These interviews often include both technical and behavioral questions designed to evaluate your data science skills and cultural fit with the company.

Q: What kinds of technical skills are required for a Data Scientist at 2nd Watch?
A: Strong proficiency in programming languages such as Python or R, experience with machine learning techniques, and a solid understanding of statistical analysis are essential. Familiarity with big data tools like Hadoop or Spark is also beneficial.

Q: How can I prepare for the interview at 2nd Watch?
A: Research the company and its services, understand the key responsibilities of the Data Scientist role, and review your technical skills. Practicing with Interview Query can help you get familiar with common interview questions and scenarios you might face.

Q: What is the company culture like at 2nd Watch?
A: 2nd Watch has a collaborative and supportive culture that values innovation and continuous improvement. The company encourages professional development and offers opportunities for growth and learning.

Q: Is remote work an option for Data Scientists at 2nd Watch?
A: Yes, 2nd Watch offers flexible working arrangements, including remote work options, which allow employees to balance their professional and personal lives effectively.

If you want more insights about the company, check out our main 2nd Watch Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about 2nd Watch’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every 2nd Watch machine learning engineer interview questions and challenges.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!