Cooper University Health Care is a premier healthcare provider dedicated to delivering top-notch medical services and innovative patient care. With a rich history and a commitment to excellence, Cooper stands as a leader in healthcare within the region.

Joining Cooper University Health Care as a Data Engineer presents an exciting opportunity to be part of a dynamic health organization. This role demands expertise in data architecture, ETL processes, and analytics to support healthcare operations and improve patient outcomes.

To assist you in preparing, Interview Query offers a comprehensive guide to navigate the interview process, featuring key insights and commonly asked questions. Prepare with us to elevate your chances of securing a position at this esteemed institution. Let's delve into the details and get you interview-ready!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Cooper University Health Care as a Data Engineer. Whether you were contacted by a 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 Cooper University Health Care 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 hiring manager may stay 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 Data Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around ETL pipelines, data warehousing, and SQL queries.

You may be given take-home assignments regarding data transformation, analytics, and optimizing data storage. Apart from these, your proficiency in programming languages like Python, Java, or Scala 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 Cooper University Health Care office. Your technical prowess, including programming, data modeling, and problem-solving 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 Engineer role at Cooper University Health Care.

Quick Tips For Cooper University Health Care Data Engineer Interviews

• Should contain three tips for interviewing for this specific company based on interview experiences.

Example:

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Cooper University Health Care interview include:

• Understand Healthcare Data: Cooper University Health Care values candidates who have experience with healthcare data. Make sure you understand the basics of healthcare data management and privacy regulations.
• Highlight Team Collaboration: Healthcare projects often require team collaboration. Be prepared to discuss how you have worked in teams and contributed to the success of complex projects.
• Showcase Your Problem-Solving Skills: Share examples of how you have tackled challenging data engineering problems in past roles. Emphasize your process and the impact of your solutions.

Typically, interviews at Cooper University Health Care vary by role and team, but commonly Data Engineer 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 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.

• 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 observe that the number of job postings per day has remained stable, but the number of applicants has been decreasing. What could be causing 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%. How would you address this issue?

• How would you assess the validity of an AB test result with a 0.04 p-value? Your company is running an AB test to increase conversion rates on a landing page. The PM reports a p-value of 0.04. How would you evaluate the validity of this result?

• How would you analyze the performance of a new LinkedIn feature without an AB test? LinkedIn has launched a feature allowing candidates to message hiring managers directly during the interview process. Due to engineering constraints, an AB test wasn't possible. How would you analyze the feature's performance?

• Should Square hire a customer success manager or offer a free trial for a new product? Square's 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. The system should also accommodate 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.

If you're eager to dive deeper into what makes Cooper University Health Care an ideal workplace for a Data Engineer, don't miss out on our comprehensive Cooper University Health Care Interview Guide. We've meticulously curated a collection of interview questions and essential insights that will give you a competitive edge. In addition, explore our guides for other roles, such as software engineer and data analyst to gain a broader perspective on the interview processes across different positions.

At Interview Query, we arm you with a robust set of tools, filling you with the knowledge, confidence, and strategic insight needed to excel in every Cooper University Health Care interview scenario.

Check out our extensive company interview guides for thorough preparation, and feel free to reach out to us with any questions.

Good luck with your interview!