Bristol-Myers Squibb is a global biopharmaceutical company focused on discovering, developing, and delivering innovative medicines for serious diseases.

As a Data Scientist at Bristol-Myers Squibb, you will play a pivotal role in leveraging data to drive insights that inform drug development and improve patient outcomes. Key responsibilities include analyzing complex healthcare datasets, developing predictive models, and employing machine learning techniques to support research and clinical decisions. You will collaborate with interdisciplinary teams to enhance data-driven strategies, ensuring that the organization remains at the forefront of scientific discovery.

To excel in this role, you should possess strong programming skills, particularly in Python, and be comfortable working with statistical algorithms and machine learning frameworks. A solid understanding of data manipulation, data visualization, and experience with cloud technologies will also be advantageous. Additionally, your ability to communicate complex data insights clearly and concisely will be crucial for effective collaboration with both technical and non-technical stakeholders.

This guide will help you prepare for your interview by providing insights into what to expect and how to align your skills and experiences with the company's mission and values.

The interview process for a Data Scientist role at Bristol-Myers Squibb is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds in several key stages:

1. HR Round

The initial step involves a conversation with a Human Resources representative. This round is designed to gauge your interest in the role and the company, as well as to discuss your background, experiences, and career aspirations. The HR representative will also evaluate your alignment with Bristol-Myers Squibb's values and culture, which is crucial for success in the organization.

2. Technical Interview

Following the HR round, candidates are given a dataset to analyze one hour prior to a technical interview. During this session, you will be expected to demonstrate your proficiency in a programming language of your choice, such as Python. The interview will focus on your ability to manipulate and analyze the data, showcasing your comfort level with coding. Additionally, you will be asked broad algorithmic questions, starting with your favorite algorithms and progressing to more complex topics. Expect inquiries about specific machine learning algorithms, such as random forests, and any relevant cloud engineering experience you may have.

3. Presentation Round

The final stage of the interview process involves a presentation where you will discuss a data science project you have previously developed, which could be from academic work, internships, or personal projects. This presentation typically lasts between 15 to 20 minutes and should highlight your experience with machine learning or deep learning models. It is advisable to select a project that is particularly relevant to Bristol-Myers Squibb, such as those involving healthcare data, drug development, natural language processing, or image analysis.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

Here are some tips to help you excel in your interview.

Understand the Role and Its Impact

Before your interview, take the time to deeply understand the role of a Data Scientist at Bristol-Myers Squibb and how it contributes to the company's mission in the pharmaceutical industry. Familiarize yourself with the types of data you might be working with, such as healthcare data, and how data science can drive advancements in drug development. This knowledge will allow you to articulate how your skills and experiences align with the company's goals.

Prepare for Technical Assessments

Expect to receive a dataset an hour before your technical interview. Use this time wisely to explore the data and prepare your approach. Make sure you are comfortable with Python, as it will be your primary tool during the assessment. Brush up on your data manipulation, analysis, and visualization skills. Be ready to demonstrate your understanding of algorithms, particularly those relevant to machine learning, such as random forests. Familiarize yourself with common data science libraries like Pandas, NumPy, and Scikit-learn.

Showcase Relevant Projects

During the interview, you will be asked to present a data science project you have worked on. Choose a project that is not only impressive but also relevant to Bristol-Myers Squibb's work. If you have experience with healthcare data, NLP, or image analysis, highlight these projects. Structure your presentation to clearly outline the problem, your approach, the tools you used, and the impact of your work. This will demonstrate your ability to apply data science in a meaningful way.

Be Ready for Interdisciplinary Collaboration

Bristol-Myers Squibb values a strong interdisciplinary team. Be prepared to discuss your experience working in collaborative environments and how you can contribute to a team that includes diverse expertise. Highlight any experiences where you worked alongside professionals from different fields, and be ready to discuss how you can leverage your data science skills to support their work.

Emphasize Cloud Engineering Experience

Given the increasing importance of cloud technologies in data science, be prepared to discuss any experience you have with cloud platforms. Familiarize yourself with common cloud services used in data science, such as AWS, Azure, or Google Cloud. If you have worked on projects that utilized cloud infrastructure, be ready to explain how it enhanced your work and the outcomes achieved.

Align with Company Culture

Bristol-Myers Squibb has a strong commitment to innovation and patient-centric solutions. During your interview, express your passion for using data science to make a positive impact in healthcare. Share your understanding of the company's values and how they resonate with your own. This alignment will help you stand out as a candidate who not only has the technical skills but also shares the company's vision.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at Bristol-Myers Squibb. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Bristol-Myers Squibb. The interview process will likely assess your technical skills in data analysis, machine learning, and programming, as well as your ability to communicate complex ideas effectively. Be prepared to discuss your past projects, particularly those relevant to healthcare or drug development.

Technical Skills

1. Can you describe a data science project you worked on that is relevant to healthcare or drug development?

Bristol-Myers Squibb is focused on innovative drug development, so they will want to see how your experience aligns with their mission.

How to Answer

Discuss the project’s objectives, the data you used, the methodologies applied, and the outcomes. Highlight any specific challenges you faced and how you overcame them.

Example

“I worked on a project analyzing patient data to predict treatment outcomes for a specific drug. I utilized machine learning algorithms to identify patterns in the data, which helped in refining the treatment protocol. The model improved prediction accuracy by 20%, leading to better patient management strategies.”

2. What programming languages are you proficient in, and how have you used them in your projects?

Your technical proficiency is crucial for this role, and they will want to know how you apply your skills in real-world scenarios.

How to Answer

Mention the programming languages you are comfortable with, particularly Python, and provide examples of how you have used them in your projects.

Example

“I am proficient in Python and R. In my last project, I used Python for data cleaning and preprocessing, leveraging libraries like Pandas and NumPy. I also implemented machine learning models using Scikit-learn, which allowed me to efficiently analyze large datasets.”

3. Explain a machine learning algorithm you frequently use and why you prefer it.

Understanding algorithms is key for a data scientist, and your reasoning for choosing one over another can demonstrate your depth of knowledge.

How to Answer

Choose an algorithm you are familiar with, explain its workings briefly, and discuss its advantages in specific scenarios.

Example

“I frequently use Random Forest due to its robustness against overfitting and its ability to handle large datasets with high dimensionality. It’s particularly useful in healthcare data where the relationships can be complex and non-linear.”

4. How do you approach feature selection in your models?

Feature selection is critical in building effective models, and they will want to know your methodology.

How to Answer

Discuss the techniques you use for feature selection and why they are important for model performance.

Example

“I typically use a combination of domain knowledge and statistical methods for feature selection. Techniques like Recursive Feature Elimination (RFE) and feature importance from tree-based models help me identify the most impactful features, ensuring that the model remains interpretable and efficient.”

5. Can you explain the concept of overfitting and how to prevent it?

Overfitting is a common issue in machine learning, and understanding it is essential for a data scientist.

How to Answer

Define overfitting and discuss strategies you use to mitigate it in your models.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. To prevent it, I use techniques such as cross-validation, regularization methods like Lasso and Ridge, and ensuring that I have a sufficiently large and diverse training dataset.”

Cloud Engineering

6. What experience do you have with cloud platforms for data science?

As cloud computing is increasingly important in data science, they will want to know your familiarity with these technologies.

How to Answer

Discuss any cloud platforms you have used, the services you utilized, and how they enhanced your data science projects.

Example

“I have experience using AWS for deploying machine learning models. I utilized services like S3 for data storage and SageMaker for building and training models. This cloud infrastructure allowed for scalable processing and easier collaboration with my team.”

7. Describe a time when you had to work with a large dataset. What challenges did you face?

Handling large datasets is a common task for data scientists, and they will want to assess your problem-solving skills.

How to Answer

Talk about the specific challenges you encountered, such as data cleaning or processing speed, and how you addressed them.

Example

“In a project analyzing clinical trial data, I faced challenges with data cleaning due to inconsistencies. I implemented a systematic approach using Python scripts to automate the cleaning process, which significantly reduced the time required and improved data quality.”

8. How do you ensure data security and compliance when working with sensitive information?

Given the nature of Bristol-Myers Squibb’s work, data security is paramount, and they will want to know your approach.

How to Answer

Discuss the practices you follow to ensure data security and compliance with regulations like HIPAA.

Example

“I prioritize data security by implementing encryption for sensitive data and ensuring that access controls are in place. I also stay updated on compliance regulations and conduct regular audits to ensure that our data handling practices meet industry standards.”

9. What tools do you use for data visualization, and why?

Data visualization is key for communicating insights, and they will want to know your preferred tools and methods.

How to Answer

Mention the tools you are familiar with and explain how they help in presenting data effectively.

Example

“I often use Tableau and Matplotlib for data visualization. Tableau allows for interactive dashboards that are great for stakeholder presentations, while Matplotlib provides flexibility for custom visualizations in Python scripts.”

10. How do you stay current with advancements in data science and machine learning?

Continuous learning is essential in the rapidly evolving field of data science, and they will want to know your approach to professional development.

How to Answer

Discuss the resources you use to stay informed, such as online courses, conferences, or research papers.

Example

“I regularly follow industry blogs, participate in webinars, and take online courses on platforms like Coursera and edX. I also attend data science meetups to network with other professionals and share knowledge about the latest trends and technologies.”

• Write 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 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?

• Write a function perm_palindrome to determine if a permutation of a string can be a palindrome. Given a string str, write a function perm_palindrome to determine whether there exists a permutation of str that is a palindrome.

• Write a function to return any subset of integers that sum to zero without including the number 0. You are given a list of integers called numbers. Write a function to return any subset of numbers where the elements sum to zero and that does not contain the number 0. If there are no combinations, return an empty list.

• Write a function to sum two numeric strings without converting them to integers. Given two strings, num_str1 and num_str2, write a function to sum the two strings together without directly converting them to integers. Return the output in string format.

• What metrics determine ride demand and supply in a ride-sharing marketplace? You work as a data scientist on a ride-sharing marketplace. What metrics would you look at to determine the demand for rides at any point? Additionally, what metrics would tell you if there is high demand and low supply? How can you determine the threshold for when there's too much demand?

• How would you measure the success of Facebook stories without using A/B testing? You are working on Facebook's stories and have found that very few users click through the story functionality. You are tasked with measuring the success of Facebook stories, but you cannot use a standard A/B test. What testing strategies and metrics would you use to determine the success of stories?

• What metrics would you use to rank Twitter users by influence? Let's say that you're given 100 Twitter users. What metrics would you use to rank each user in influence? How would you quantify the influence of a Twitter user?

• What does the distribution of time spent per day on Facebook look like? Describe the distribution of time spent per day on Facebook. What metrics would you use to characterize this distribution?

• Show that if (f_X) is a strictly decreasing function, then (m \geq \mu). Given a continuous random variable (X) with probability density function (f_X(x)), mean (\mu), and median (m), prove that if (f_X) is strictly decreasing, then (m \geq \mu).

• What is the probability of getting a pair in a hand of (N) cards? If you draw (N) cards without replacement from a standard 52-card deck, compute the probability of getting a pair (two cards of the same rank).

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms and explain the tradeoffs between using a bagging algorithm and a boosting algorithm. Provide examples to illustrate the differences.

• How would you design an ML system to extract, transform, and store data from APIs for downstream models? As a machine learning engineer for a large bank, you have access to Reddit and Bloomberg APIs. Design a system that extracts data from these APIs, transforms it, and stores it in a format usable by downstream modeling teams for various applications like risk assessment and marketing.

Navigating the interview process for a Data Scientist position at Bristol-Myers Squibb reveals a multifaceted and comprehensive experience aimed at uncovering technical prowess, problem-solving abilities, and cultural fit. From initial HR screenings to detailed technical rounds and insightful panel interviews that may include presentations, the process ensures a deep dive into candidates' skills and their alignment with the organization's goals. If you want more insights about the company, check out our main Bristol-Myers Squibb Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Bristol-Myers Squibb’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Bristol-Myers Squibb Data Scientist interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!