Philips Data Scientist Interview Questions + Guide in 2024

Philips, a global health technology leader, is committed to improving people’s health and well-being through meaningful innovation. Established for over 130 years, Philips operates in over 100 countries, focusing on areas like precision diagnosis, image-guided therapy, and connected care.

As a Data Scientist at Philips, you’ll engage in high-impact projects involving the analysis of large-scale biomedical datasets and the development of machine learning algorithms. Expect a comprehensive interview process, typically involving multiple technical rounds and discussions about your past projects and experiences.

If you’re driven by the idea of leveraging data to enhance healthcare solutions, this guide will walk you through the interview process, common Philips data scientist interview questions, and valuable tips to help you succeed. Let’s get started!

The interview process usually depends on the role and seniority; however, you can expect the following on a Philips data scientist interview:

Recruiter/Hiring Manager Call Screening

If your application is shortlisted, a recruiter from Philips’ Talent Acquisition Team will reach out for an initial phone call. This call aims to verify your experiences, skills, and overall fit for the company. Expect questions about your background, projects, and technical skills, especially in mathematics, programming, and machine learning. The call may also include behavioral questions to assess your soft skills and cultural fit.

Typical questions:

1. Tell me about yourself.
2. What projects are you currently working on?
3. What interests you about working at Philips?

This screening call usually takes about 30 minutes.

Technical Virtual Interview

After successfully navigating the recruiter call, you’ll be invited to a technical virtual interview. This round will be conducted via video conference and may include screen-sharing sessions where you’ll solve real-time coding tasks. The session will delve deep into your technical skills, projects, and domain knowledge.

Topics to prepare for include:

1. Coding problems and algorithms.
2. Questions about your experience with machine learning models like logistic regression, decision trees, neural networks, and time series (ARIMA).
3. Discussing your past projects in detail, particularly those mentioned in your resume.
4. Concepts such as precision/recall ratio, SVM, and Bayesian sampling.

Onsite Interview Rounds

If you pass the technical virtual interview, you’ll be invited to attend onsite interview rounds at Philips. These may include multiple interviews with team members across different levels and departments. Expect intense scrutiny of your CV and detailed discussions about your projects. Additionally, you might be asked to undertake a take-home exercise or participate in a presentation and Q/A session.

Key areas assessed:

1. Technical depth in machine learning, modeling techniques, and statistical analysis.
2. Your understanding of clinical domains and healthcare data analytics.
3. How well your skills align with Philips’ current projects and business goals.

Typically, interviews at Philips vary by role and team, but common data scientist interviews follow a fairly standardized process across these question topics.

1. 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.

2. 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 whether an individual will undergo major health issues based on a set of health features.

3. How does a high correlation between two features affect their importance in a random forest?

If two features are highly correlated in a random forest, how will this correlation impact their measurement of feature importance?

4. Why is the number of job applicants decreasing despite stable job postings?

You are looking at job board metrics and notice that while the number of job postings per day has remained stable over the last few months, the number of applicants has been steadily decreasing. Why might this be happening?

5. How would you assess the validity of a .04 p-value in an AB test?

Your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM finds a .04 p-value in the results. How would you assess the validity of this result?

6. How would you analyze the performance of a new LinkedIn feature without an AB test?

As a data scientist at LinkedIn, you are working on a product that allows candidates to message hiring managers directly during the interview process. Due to engineering constraints, the company can’t AB test the feature before launching it. How would you analyze how the feature is performing?

7. 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 instituting a free trial instead. What would be your recommendation on utilizing a customer success manager versus a free trial to get new or existing customers to use the new product?

8. How could you promote Instagram through the Facebook app?

You work on the growth team at Facebook and are tasked with promoting Instagram from within the Facebook app. Where and how could you promote Instagram through Facebook?

9. Build a logistic regression model from scratch using gradient descent and log-likelihood.

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.

10. 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.

11. 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.

12. 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.

Here are some tips for the Philips data scientist interview which can help you ace the process. These are based on some interview experiences.

1. Deep Dive into Your Projects:

   • Be prepared to discuss your projects in great detail. Interviewers may ask questions that require you to explain the technical and business aspects of your projects.
   • Focus on the impact your work had and the methodologies you used.

2. Strong Fundamentals in Machine Learning:

   • Have a solid understanding of machine learning basics, including algorithms, statistical modeling, and data preprocessing.
   • Review common machine learning concepts and their applications in healthcare.

3. Behavioral and Soft Skills:

   • Interviews at Philips will also test your soft skills such as communication, teamwork, and problem-solving abilities.
   • Practice responding to behavioral questions with answers that reflect Philips’ values and your ability to collaborate effectively within cross-functional teams.

What is the average salary for a Data Scientist at Philips?

What kind of projects do Data Scientists at Philips work on?

Data Scientists at Philips work on a range of projects, including analyzing large biomedical datasets, developing predictive models, and creating data visualizations. Projects aim to improve remote cardiac monitoring solutions, patient monitoring system usage, and healthcare informatics.

What is the company culture like at Philips?

Philips has a dynamic and cross-functional team environment that values research, innovation, and collaboration. The company is committed to improving healthcare through technology and places a strong emphasis on inclusivity and diversity.

How can I best prepare for an interview at Philips?

To prepare, focus on honing your technical skills, particularly in Python and machine learning, and review your past projects in detail. Be ready to discuss your resume extensively and understand healthcare data analytics. Research Philips’ active projects and familiarize yourself with the company’s mission and values.

As a forward-thinking health technology leader, Philips continuously seeks adept and visionary data scientists to drive revolutionary advancements in healthcare. By mastering the technical, managerial, and clinical-centric rounds described, and demonstrating proficiency in areas such as machine learning, statistical modeling, and project execution, you will distinctly stand out as an exceptional candidate.

You may also visit our career page and discover the life-changing projects you could be a part of. Your perfect role awaits you at Philips. Embark on a journey to do the work of your life to help the lives of others.

Best of luck with your interview, and we can’t wait to see how you will innovate with us!