Barnabas Health is a major player in the healthcare industry, providing comprehensive medical services and innovations aimed at enhancing patient care. Known for its cutting-edge healthcare solutions and dedicated staff, Barnabas Health continues to expand its impact in the medical field.

As a Business Intelligence Analyst at Barnabas Health, you'll be at the forefront of transforming data into actionable insights to drive strategic decision-making. This role demands proficiency in data analytics, reporting tools, SQL, and a deep understanding of healthcare metrics. You'll collaborate with various teams to identify trends, optimize performance, and address healthcare challenges through data-driven solutions.

If you're interested in joining Barnabas Health's mission to improve patient outcomes, this guide is for you. We'll walk you through the interview process, share common interview questions for the Business Intelligence position, and provide valuable tips to help you succeed. Let’s get started on your journey with Interview Query!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Barnabas Health as a Business Intelligence professional. Whether you were contacted by a Barnabas Health 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 Barnabas Health 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 Barnabas Health Business Intelligence 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 Barnabas Health Business Intelligence role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Barnabas Health’s data systems, ETL pipelines, and SQL queries.

In the case of Business Intelligence 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 Barnabas Health 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 Business Intelligence role at Barnabas Health.

Quick Tips For Barnabas Health Business Intelligence Interviews

• Know Barnabas Health's Systems and Data Needs: Investigate Barnabas Health’s data infrastructure and understand their data pipelines, tools, and technologies. This will help you align your experiences with their needs.
• Prepare for Behavioral Questions: Barnabas Health places a significant emphasis on cultural fit. Be prepared to respond to behavioral questions that reflect the company’s values and ethos.
• Mock Technical Questions: Plan to brush up on any technical skills and try as many practice interview questions possible through platforms like Interview Query. Practice your proficiency in SQL, data analysis, and visualization techniques.

Typically, interviews at Barnabas Health vary by role and team, but commonly Business Intelligence interviews follow a fairly standardized process across these question topics.

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• How would you design a machine learning model to classify major health issues based on health features? You work as a machine learning engineer for a health insurance company. Design a model that classifies if an individual will undergo major health issues based on a set of health features.

• What metrics and statistical methods would you use to identify dishonest users in a sports tracking app? You work for a company with a sports app that tracks running, jogging, and cycling data. Formulate a method to identify users who might be cheating, such as driving a car while claiming to be on a bike ride. Specify the metrics and statistical methods you would analyze to detect athletic anomalies.

• Develop a function str_map to determine if a one-to-one correspondence exists between characters of two strings at the same positions. Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

• Build a logistic regression model from scratch using gradient descent and log-likelihood as the loss function. Create a logistic regression model from scratch without an intercept term. Use basic gradient descent (with Newton's method) for optimization and the log-likelihood as the loss function. Do not include a penalty term. You may use numpy and pandas but not scikit-learn. Return the parameters of the regression.

• Why are job applications decreasing despite stable job postings? You are analyzing a job board where the number of job postings per day has remained stable, but the number of applicants has been steadily decreasing. What could be the reasons for this trend?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook informs you that friend requests have decreased by 10%. What steps would you take to investigate and address this issue?

• How would you assess the validity of a .04 p-value in an AB test? Your company is running an AB test on a feature to increase conversion rates on the landing page. The PM reports a p-value of .04. How would you evaluate the validity of this result?

• How would you analyze the performance of a new LinkedIn feature without an AB test? You are a data scientist at LinkedIn, and a new feature allows candidates to message hiring managers directly during the interview process. Due to engineering constraints, an AB test was not conducted before launch. How would you analyze the feature's performance?

• Should Square hire a customer success manager or offer a free trial for a new product? You are in charge of Square's small business division. The CEO wants to hire a customer success manager for a new software product, while another executive suggests offering a free trial instead. What would be your recommendation?

• How would you build a fraud detection model using a dataset of 600,000 credit card transactions? Imagine you work at a major credit card company and are given a dataset of 600,000 credit card transactions. Describe your approach to building a fraud detection model.

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• How would you tackle multicollinearity in multiple linear regression? Describe the methods you would use to address multicollinearity in a multiple linear regression model.

• How would you design a facial recognition system for employee clock-in and secure access? You work as an ML engineer for a large company that wants to implement a facial recognition system for employee clock-in, clock-out, and access to secure systems, including temporary contract consultants. How would you design this system?

• How would you handle data preparation for building a machine learning model using imbalanced data? Explain the steps you would take to prepare data for building a machine learning model when dealing with imbalanced data.

For comprehensive insights into Barnabas Health and their Business Intelligence position, check out our Barnabas Health Interview Guide, where we delve into numerous interview questions you might face. Additionally, we've crafted resources for other roles like data analyst and software engineer, providing a thorough overview of Barnabas Health's interview process across various positions.

At Interview Query, we equip you with a robust toolkit, empowering you with the knowledge, confidence, and strategic guidance required to excel in Barnabas Health’s Business Intelligence interview stages.

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

Good luck with your interview!