iFoodDecisionSciences is a cutting-edge technology company revolutionizing data analytics and decision-making in the food and agriculture industry. By leveraging advanced data science, iFoodDecisionSciences aims to enhance food safety, quality, and operational efficiency for its clients.

Joining iFoodDecisionSciences as a Business Intelligence professional means diving deep into data analytics to support strategic decision-making. This role demands strong technical skills in data manipulation, visualization, and statistical analysis. You'll work to transform complex data sets into actionable insights, assisting in optimizing processes and driving the company's growth.

Considering a career at this innovative company? This guide is for you. We will outline the interview process, share commonly asked questions, and provide tips to help you succeed. Let’s embark on this journey with Interview Query!

Submitting Your Application

The first step in the interview process for a Business Intelligence position at iFoodDecisionSciences involves submitting a compelling application that reflects your technical skills and interest in joining the team. Whether you were contacted by a recruiter or took 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. Additionally, be sure to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted candidates, a recruiter from the iFoodDecisionSciences Talent Acquisition Team will reach out to verify key details such as your experience and skill level. Behavioral questions may also be a part of the screening process.

In some instances, the hiring manager may also be present during this initial screening to answer any queries you have about the role and the company itself. They may also touch on surface-level technical and behavioral aspects.

This initial recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will garner you an invitation for the technical screening round. For the Business Intelligence role, this typically involves a virtual interview including video conferencing and screen-sharing. This 1-hour-long interview may cover iFoodDecisionSciences’ data systems, ETL pipelines, and SQL queries.

You may also be given take-home assignments related to analytics, product metrics, and data visualization. Your proficiency in hypothesis testing, probability distributions, and foundational machine learning concepts may also be evaluated during this round.

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

Onsite Interview Rounds

Following a secondary recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, dependent on the role, will occur during your visit to the iFoodDecisionSciences office. Throughout these interviews, your technical skills, including programming and ML modeling capabilities, will be evaluated against other final candidates.

If you were assigned any take-home exercises, you might also have to present your findings during the onsite interview for the Business Intelligence role at iFoodDecisionSciences.

Quick Tips For iFoodDecisionSciences Business Intelligence Interviews

• Brush Up on Technical Skills: Ensure you are well-prepared with the necessary technical skills. Practicing interview questions and mock interviews on platforms like Interview Query can be invaluable.
• Understand the Company’s Data Systems: Delve into understanding how iFoodDecisionSciences handles their data systems, pipelines, and SQL databases. This knowledge can help you in both technical and behavioral interviews.
• Focus on Analytical and Visualization Skills: Since the role might include tasks related to analytics and data visualization, make sure you are proficient in these areas to tackle any assignments or scenario-based questions effectively.

Typically, interviews at Ifooddecisionsciences vary by role and team, but commonly Business Intelligence 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 merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: What's the time complexity?

• Create a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Develop a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

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

• 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 design this test?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

• Why might the number of job applicants be decreasing while job postings remain the same? You observe that job postings per day have remained constant, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common problems in "messy" datasets.

• Is this a fair coin based on 10 flips resulting in 8 tails and 2 heads? You flipped a coin 10 times, resulting in 8 tails and 2 heads. Determine if the coin is fair based on this outcome.

• How would you write a function to calculate sample variance for a list of integers? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. For example, given test_list = [6, 7, 3, 9, 10, 15], the function should return 13.89.

• Is there anything suspicious about an A/B test with 20 variants where one is significant? Your manager ran an A/B test with 20 different variants and found one significant result. Would you find anything suspicious about these results?

• How would you find the median of a list where more than 50% of the elements are the same? Given a list of sorted integers where more than 50% of the list is comprised of the same repeating integer, write a function to return the median value in (O(1)) computational time and space. For example, given li = [1,2,2], the function should return 2.

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

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice for this problem?

• How would you evaluate the performance of a decision tree model before and after deployment? If you decide to use a decision tree model for predicting loan repayment, how would you assess its performance before deployment and monitor it after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss why you might choose random forest over logistic regression for certain problems.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In what scenarios would you prefer a bagging algorithm over a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? If 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?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? You are tasked with building a spam classifier for emails. After creating a V1 of the model, what metrics would you use to evaluate its accuracy and validity?

Applying for the Business Intelligence position at Ifooddecisionsciences can be a transformative career move. To thoroughly prepare and increase your chances of acing the interview, check out our comprehensive Ifooddecisionsciences Interview Guide, where we've curated potential interview questions and insights. We also offer tailored interview guides for other roles, such as software engineer and data analyst, providing a deeper understanding of Ifooddecisionsciences' interview process across different positions.

At Interview Query, empower yourself with essential resources that enhance your interview readiness, boost your confidence, and equip you to tackle any challenge. For more targeted preparation, explore our full range of company interview guides, and don’t hesitate to reach out if you need further assistance.

Good luck with your interview!