QuintilesIMS, a leading global provider of advanced analytics, technology solutions, and clinical research services, continuously revolutionizes the life sciences industry. The company strives to push the boundaries of human science and data science to make a significant impact, helping customers create a healthier world.

As a Data Engineer at QuintilesIMS, you will play a crucial role in shaping the future of healthcare. This position requires a strong foundation in technical skills, including Python, SQL, and cloud computing, as well as experience with data task orchestration tools like Airflow. You will be responsible for constructing data pipelines, maintaining data warehouses, and developing scalable data products to streamline and enhance data-driven decision-making processes.

This guide by Interview Query aims to prepare you for the interview process at QuintilesIMS, offering insights into the types of questions to expect and tips for success. Let's get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Quintilesims 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 Quintilesims 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 data engineer 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 20-30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Quintilesims 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 data systems, ETL pipelines, and SQL queries.

In some instances, a live coding session focused on MLOps, Python, or SQL might be incorporated. Your proficiency against data engineering fundamentals such as data modeling, data task orchestration, and cloud storage interactions may also be assessed during the round.

Apart from these, make sure you are comfortable with topics like Airflow, CRON jobs, dependency mapping, and familiarity with cloud computing technologies (e.g., GCP).

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 Quintilesims office. Your technical prowess, including programming and data engineering 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 Quintilesims.

Quick Tips For Quintilesims Data Engineer Interviews

• Practice Problem-Solving: Focus on algorithms, data structures, and SQL queries. Platforms like Interview Query provide practice questions and mock interviews to help you prepare.

• Be Familiar with Tools and Frameworks: Commonly used tools in this role include Airflow, GCP, and various data orchestration frameworks. Brush up on these to give relevant answers during technical rounds.

• Behavioral Preparation: Quintilesims values a collaborative work culture. Prepare to answer behavioral questions that align with their core values and showcase your ability to work well within a team.

Typically, interviews at IQVIA vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

• 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 finds 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?

• 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.

• 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.

• 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.

What is the average salary for a Data Engineer at Quintilesims?

Q: What is the interview process for the Data Engineer position at QuintilesIMS like?
The interview process typically involves three stages: an initial HR phone screen, followed by one or more technical interviews, and then a final round with senior management. You may also be required to complete technical exercises or case studies.

Q: What kind of technical skills are required for the Data Engineer position?
To excel in this role, you need strong skills in Python, SQL, and shell scripting. Experience with data task orchestration tools like Airflow, CRON, or Prefect, as well as familiarity with cloud storage solutions like GCS or S3, are highly desirable.

Q: What is the company culture like at QuintilesIMS?
QuintilesIMS values innovation, collaboration, and diversity. The engineering team is multinational and focused on pushing boundaries in healthcare marketing analytics. Employees are encouraged to learn, grow, and implement new knowledge quickly.

Q: How can I prepare for an interview at QuintilesIMS?
To prepare, it's essential to practice your data engineering skills and familiarize yourself with the company's technologies and methodologies. Take advantage of platforms like Interview Query for mock interviews and technical problems practice. Also, read up on the company's mission and recent projects.

Q: What can I expect in terms of job responsibilities as a Data Engineer at QuintilesIMS?
Job responsibilities include constructing data pipelines, maintaining and optimizing database schemas, performing ad-hoc data analysis, developing workflows, and documenting data architecture. You will also provide guidance on data best practices and automate long-running processes.

The interview process at IQVIA for the Data Engineer position is a comprehensive journey that will test your technical skills, problem-solving abilities, and cultural fit within the team. While the experiences shared by candidates vary, the overall sentiment highlights the importance of both technical acumen and adaptability to the company’s dynamic environment.

If you are preparing for an interview with IQVIA, make sure to leverage the resources available on Interview Query. We’ve compiled a variety of interview questions that may come up, and offer insights into the interview process for various roles, including technical positions like data engineers. At Interview Query, we empower you to excel in your interview through strategic guidance and valuable resources tailored to IQVIA’s hiring process.

Good luck with your interview!