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Amgen Data Scientist Interview Questions + Guide in 2025

Amgen Data Scientist Interview Questions + Guide in 2025

Overview

Amgen is a pioneering biotechnology company committed to serving patients living with serious illnesses through the development of innovative medicines and therapies.

As a Data Scientist at Amgen, you will play a crucial role in harnessing data to drive insights and enhance decision-making processes across various therapeutic areas including Oncology, Inflammation, General Medicine, and Rare Disease. Your responsibilities will include collaborating with cross-functional teams to develop predictive and prescriptive models, utilizing advanced data analytics techniques and tools such as Python, SQL, and machine learning frameworks. You will also engage in the visualization of data to provide actionable insights and support the automation of processes, all while ensuring compliance with regulatory standards.

To excel in this role, you should possess strong analytical skills, proficiency in programming, and a solid understanding of statistical methods and machine learning algorithms. Experience in the biopharmaceutical industry, particularly in areas such as pharmacovigilance or clinical data analysis, will be highly advantageous. Additionally, a collaborative mindset and excellent communication skills are essential, as you will be expected to interact with diverse teams and influence stakeholders at various levels.

This guide will equip you with the necessary insights and preparation strategies to navigate the interview process effectively, enabling you to showcase your expertise and alignment with Amgen's mission and values.

Amgen Data Scientist Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Amgen. The interview process will likely assess both technical and behavioral competencies, focusing on your ability to apply data science methodologies in a biopharmaceutical context. Candidates should be prepared to discuss their experience with machine learning, statistics, data analytics, and their ability to collaborate across teams.

Machine Learning

1. Can you describe a machine learning project you worked on and the impact it had?

This question aims to gauge your practical experience with machine learning and its application in real-world scenarios.

How to Answer

Discuss the project’s objectives, the methodologies you employed, and the results achieved. Highlight any specific metrics that demonstrate the project's success.

Example

“I worked on a project to predict patient responses to a new drug using historical clinical trial data. By implementing a random forest model, we achieved an accuracy of 85%, which helped the team prioritize candidates for the next trial phase, ultimately reducing time to market by three months.”

2. How do you handle overfitting in your models?

This question tests your understanding of model performance and generalization.

How to Answer

Explain techniques such as cross-validation, regularization, or pruning that you use to mitigate overfitting.

Example

“I typically use cross-validation to assess model performance on unseen data. Additionally, I apply regularization techniques like Lasso or Ridge regression to penalize overly complex models, ensuring they generalize well to new data.”

3. What is your experience with deep learning frameworks?

This question assesses your familiarity with advanced machine learning techniques.

How to Answer

Mention specific frameworks you’ve used, the types of problems you solved, and any relevant results.

Example

“I have experience using TensorFlow and PyTorch for developing convolutional neural networks for image classification tasks. In one project, I improved classification accuracy by 15% compared to traditional machine learning methods.”

4. Can you explain the difference between supervised and unsupervised learning?

This question evaluates your foundational knowledge of machine learning concepts.

How to Answer

Define both terms clearly and provide examples of each.

Example

“Supervised learning involves training a model on labeled data, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior without predefined categories.”

Statistics & Probability

1. How do you assess the statistical significance of your results?

This question tests your understanding of statistical methods.

How to Answer

Discuss the use of p-values, confidence intervals, or hypothesis testing in your analysis.

Example

“I assess statistical significance by conducting hypothesis tests and calculating p-values. For instance, in a recent A/B test, I found a p-value of 0.03, indicating that the observed difference in conversion rates was statistically significant at the 5% level.”

2. What statistical methods do you commonly use in your analyses?

This question aims to understand your toolkit for data analysis.

How to Answer

List the statistical methods you are familiar with and provide context for their use.

Example

“I frequently use regression analysis for predictive modeling, ANOVA for comparing means across groups, and chi-square tests for categorical data analysis. Each method helps me derive insights from data effectively.”

3. Can you explain the concept of Bayesian statistics?

This question evaluates your knowledge of advanced statistical concepts.

How to Answer

Provide a brief overview of Bayesian statistics and its applications.

Example

“Bayesian statistics involves updating the probability of a hypothesis as more evidence becomes available. It’s particularly useful in scenarios where prior knowledge can inform the analysis, such as in clinical trials where previous studies guide current decision-making.”

Data Analytics & Tools

1. What data visualization tools have you used, and how do you choose which to use?

This question assesses your experience with data visualization.

How to Answer

Mention specific tools and the criteria you use for selecting them based on the audience and data type.

Example

“I have used Tableau and Spotfire for data visualization. I choose Tableau for its user-friendly interface when presenting to stakeholders, while I prefer Spotfire for its advanced analytics capabilities when working with complex datasets.”

2. Describe your experience with SQL and how you use it in your projects.

This question evaluates your technical skills in data manipulation.

How to Answer

Discuss your proficiency with SQL and provide examples of queries you’ve written.

Example

“I am proficient in SQL and use it to extract and manipulate data from relational databases. For instance, I wrote complex queries involving joins and subqueries to analyze patient data, which helped identify trends in treatment efficacy.”

3. How do you ensure data quality in your analyses?

This question tests your understanding of data integrity.

How to Answer

Explain the steps you take to validate and clean data before analysis.

Example

“I ensure data quality by implementing validation checks, such as verifying data types and ranges, and conducting exploratory data analysis to identify anomalies. Additionally, I use data cleaning techniques to handle missing values and outliers.”

4. Can you discuss a time when you had to present complex data findings to a non-technical audience?

This question assesses your communication skills.

How to Answer

Describe the situation, your approach to simplifying the information, and the outcome.

Example

“I presented findings from a predictive model to the marketing team. I focused on visualizations to illustrate key insights and avoided technical jargon, which helped them understand the implications for their campaigns, leading to a successful strategy adjustment.”

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Topics
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Machine Learning
Medium
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Machine Learning
Hard
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Algorithms
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View all Amgen Data Scientist questions

Amgen Data Scientist Interview Tips

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

Emphasize Collaboration and Communication Skills

Amgen values a collaborative and innovative culture, so be prepared to discuss your experiences working in cross-functional teams. Highlight specific instances where you successfully collaborated with scientists, engineers, or other stakeholders to achieve a common goal. Additionally, demonstrate your ability to communicate complex technical concepts to non-technical audiences, as this is crucial in a multidisciplinary environment.

Prepare for Behavioral Questions

Expect a significant focus on behavioral questions during your interview. Reflect on your past experiences and prepare to discuss challenges you've faced, how you overcame them, and the impact of your actions. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey not just what you did, but also the thought process behind your decisions.

Showcase Your Technical Expertise

While some interviews may lean heavily on behavioral questions, others will require you to demonstrate your technical knowledge. Be ready to discuss your experience with data analytics tools and programming languages relevant to the role, such as Python, SQL, and machine learning frameworks. Prepare to explain your approach to solving data-related problems and how you have applied statistical methods in real-world scenarios.

Understand the Company’s Mission and Values

Amgen’s mission to serve patients living with serious illnesses is at the core of its operations. Familiarize yourself with the company’s therapeutic areas and recent advancements in biotechnology. Be prepared to articulate how your skills and experiences align with Amgen’s mission and how you can contribute to their goals.

Be Ready for Case Studies or Technical Scenarios

Some candidates reported being asked to solve case studies or technical scenarios during their interviews. Brush up on your problem-solving skills and be prepared to think critically on the spot. Practice articulating your thought process clearly and logically, as this will demonstrate your analytical capabilities and ability to work under pressure.

Adaptability is Key

Amgen operates in a fast-paced and evolving industry. Be prepared to discuss how you have adapted to changing circumstances in your previous roles. Highlight your ability to learn new technologies quickly and your willingness to embrace change, as this will resonate well with the company’s innovative culture.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers that reflect your interest in the role and the company. Inquire about the team dynamics, ongoing projects, or how the data science team collaborates with other departments. This not only shows your enthusiasm but also helps you gauge if the company culture aligns with your values.

Follow Up with Gratitude

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the role and briefly mention a key point from the interview that resonated with you. This small gesture can leave a positive impression and reinforce your enthusiasm for the position.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically proficient but also a great fit for Amgen’s collaborative and patient-focused culture. Good luck!

Amgen Data Scientist Interview Process

The interview process for a Data Scientist role at Amgen is structured to assess both technical and behavioral competencies, ensuring candidates align with the company's mission and values. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, usually conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and focuses on understanding your background, skills, and motivations for applying to Amgen. The recruiter will also provide insights into the company culture and the specific role, allowing you to gauge if it aligns with your career aspirations.

2. Technical Interview

Following the initial screening, candidates typically undergo a technical interview, which may be conducted virtually. This interview is often led by a hiring manager or a senior data scientist. Expect to discuss your experience with statistical analysis, machine learning, and programming languages such as Python and SQL. You may also be asked to solve case studies or technical problems relevant to the role, demonstrating your analytical thinking and problem-solving skills.

3. Behavioral Interview

In addition to technical skills, Amgen places a strong emphasis on cultural fit and collaboration. The behavioral interview will focus on your past experiences, particularly how you handle challenges, work in teams, and contribute to projects. Be prepared to share specific examples that highlight your ability to adapt, communicate effectively, and drive results in a collaborative environment.

4. Onsite Interview

The final stage often includes an onsite interview, which may be conducted in a panel format with multiple team members. This round typically combines technical assessments with behavioral questions, allowing you to showcase your expertise and interpersonal skills. You may also be asked to present a project or case study that demonstrates your data science capabilities and how they can be applied to Amgen's objectives.

5. Final Discussion

After the onsite interviews, there may be a final discussion with senior leadership or team members to assess your fit within the broader organizational context. This is an opportunity for you to ask questions about the team dynamics, ongoing projects, and how your role would contribute to Amgen's mission.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and collaborative experiences.

What Amgen Looks for in a Data Scientist

A/B TestingAlgorithmsAnalyticsMachine LearningProbabilityProduct MetricsPythonSQLStatistics
Amgen Data Scientist
Average Data Scientist

1. Write a function calculate_rmse to calculate the root mean squared error of a regression model.

The function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

2. Write a query to get the last transaction for each day from a table of bank transactions.

Given a table of bank transactions with columns id, transaction_value, and created_at, write a query to get the last transaction for each day. The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.

3. Write a function random_key that returns a key at random with a probability proportional to the weights.

Given a dictionary with weights, write a function random_key that returns a key at random with a probability proportional to the weights.

4. Write a function to get a sample from a standard normal distribution.

Create a function to generate a sample from a standard normal distribution.

5. Write a function nearest_entries to find the closest element to N and return k-next and k-previous elements.

Given a sorted list of integers ints with no duplicates, write an efficient function nearest_entries that takes in integers N and k and returns the element closest to N along with the k-next and k-previous elements of the list.

6. How would you explain what a p-value is to someone who is not technical?

Explain the concept of a p-value in simple terms to someone without a technical background. Use analogies or everyday examples to make it understandable.

7. How many more samples would we need to decrease the margin of error from 3 to 0.3?

Given a sample size (n) with a margin of error of 3, calculate the additional number of samples required to reduce the margin of error to 0.3.

8. How would you determine if the results of an AB test on a landing page redesign are statistically significant?

Describe the process of analyzing AB test results to determine if the observed differences in click-through rates are statistically significant. Include steps such as hypothesis testing and p-value calculation.

9. What metrics/graphs/models would you use to analyze churn behavior for Netflix’s pricing plans?

Netflix has two pricing plans: $15/month or $100/year. An executive wants to analyze the churn behavior of users subscribing to either plan. What kinds of metrics, graphs, or models would you build to provide an overarching view of subscription performance?

10. How would you build a model to predict which merchants DoorDash should acquire in a new market?

As a data scientist at DoorDash, how would you build a model to predict which merchants the company should target for acquisition when entering a new market?

11. How would you value the benefit of keeping a hit TV show on Netflix?

Netflix executives are considering renewing a deal with another TV network for exclusive streaming rights to a hit TV series. The show has been on Netflix for a year. How would you approach valuing the benefit of keeping this show on Netflix?

12. How would you measure the success of LinkedIn’s newsfeed ranking algorithm?

As a data scientist on the engagement team, how would you measure the success of LinkedIn’s newsfeed ranking algorithm? Additionally, if some success metrics are improving while others are declining, how would you approach this situation?

13. How would you assign point values to letters in Spanish Scrabble without knowing Spanish?

If you need to build Scrabble for Spanish users and don’t know Spanish, how would you determine the point values for each letter?

How to Prepare for an Amgen Data Scientist Interview

Here are some quick tips to help you stand out during interviews:

  • Refresh Machine Learning Fundamentals: Amgen significantly emphasizes machine learning background. Make sure you are comfortable with prediction models and can articulate your approach to the machine learning interview questions you’ll get asked.

  • Understand Healthcare Data: Familiarize yourself with medical data, such as drug prescription data, as you may be asked questions related to healthcare analytics. You can also try these healthcare data science projects to become more familiar with how these data work.

  • Be Prepared for Behavioral Questions: Given that some candidates focused extensively on behavioral questions, be ready to discuss past experiences, especially those involving hardships and how you overcame them.

FAQs

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

$128,817

Average Base Salary

$70,058

Average Total Compensation

Min: $107K
Max: $164K
Base Salary
Median: $128K
Mean (Average): $129K
Data points: 26
Min: $12K
Max: $128K
Total Compensation
Median: $70K
Mean (Average): $70K
Data points: 2

View the full Data Scientist at Amgen salary guide

What is the company culture like at Amgen?

Amgen is committed to innovation and collaboration, aiming to make a significant impact in the biopharmaceutical industry. The company fosters a supportive work environment that values continuous learning, mentoring, and professional growth. Employees work together to serve patients and achieve organizational goals.

What can I expect in terms of professional growth and benefits at Amgen?

Amgen offers a comprehensive Total Rewards Plan, including health and welfare plans, retirement savings plans, and career development opportunities. Employees can also benefit from flexible work models, stock-based incentives, annual bonuses, and award-winning time-off plans.

Conclusion

Landing a position as a Data Scientist at Amgen is an exciting opportunity to be at the forefront of biopharmaceutical innovation and digital transformation, and we hope this interview guide is helpful for your preparation.

For more information about Amgen, please refer to our main interview guide, which provides extensive details about the company and its interview process for tech positions.

Good luck with your interview!