10X Genomics is at the forefront of biological research, dedicated to advancing human health through innovative technologies that accelerate understanding in life sciences.

The Data Scientist role at 10X Genomics is pivotal in transforming data into actionable insights that drive strategic decision-making across the organization. This position involves collaborating with cross-functional teams to enhance marketing effectiveness, develop robust data frameworks, and implement advanced analytics techniques. Key responsibilities include building business cases, conducting market modeling, and developing campaign attribution models, all aimed at optimizing marketing strategies and improving overall performance. The ideal candidate should possess strong analytical skills, proficiency in programming languages such as R, SQL, and Python, and experience in applied statistics and machine learning. Additionally, they should be adept at data visualization and reporting tools, with a proven ability to translate complex data into clear, impactful business recommendations. A passion for biology and a commitment to the company’s mission will further distinguish an exceptional candidate.

This guide will help you prepare for your interview by providing insights into the role's expectations and the skills that 10X Genomics values, giving you a competitive edge in showcasing your qualifications and alignment with the company's mission.

The interview process at 10X Genomics begins with an initial screening, typically conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and serves to assess your fit for the company culture, as well as to discuss your background, skills, and career aspirations. The recruiter will also provide insights into the role and the company’s mission, ensuring that you have a clear understanding of what it means to work at 10X Genomics.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted through a video call. This stage focuses on evaluating your analytical skills and technical expertise relevant to data science. You can expect to engage in discussions around data manipulation using programming languages such as R, SQL, and Python, as well as applied statistics and machine learning techniques. Be prepared to demonstrate your problem-solving abilities through practical exercises or case studies that reflect real-world scenarios you might encounter in the role.

3. Behavioral Interviews

After successfully completing the technical assessment, candidates will participate in one or more behavioral interviews. These interviews are designed to gauge your interpersonal skills, teamwork, and how you handle challenges in a fast-paced environment. Interviewers will be interested in your past experiences and how they align with the responsibilities of the data scientist role, particularly in terms of driving data-driven insights and collaborating with cross-functional teams.

4. Onsite Interview

The final stage of the interview process is the onsite interview, which typically consists of multiple rounds with various team members, including data scientists, marketing analysts, and possibly executives. Each round will last approximately 45 minutes and will cover a mix of technical, analytical, and behavioral questions. You may be asked to present your previous work or case studies that demonstrate your ability to turn data analysis into actionable business insights. This is also an opportunity for you to ask questions about the team dynamics and the company’s strategic goals.

As you prepare for these interviews, it’s essential to familiarize yourself with the types of questions that may be asked, particularly those that relate to your technical skills and past experiences.

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

Emphasize Cross-Disciplinary Collaboration

At 10X Genomics, the role of a Data Scientist is deeply integrated with various teams, including Marketing and Product Management. Highlight your experience in cross-functional collaboration and how you have successfully worked with diverse teams to drive data-driven insights. Be prepared to discuss specific examples where your contributions led to improved decision-making or enhanced marketing strategies.

Showcase Your Technical Proficiency

Given the emphasis on advanced analytical skills, ensure you are well-versed in R, SQL, and Python, particularly in data manipulation and statistical modeling. Prepare to discuss your experience with machine learning techniques and how you have applied them in real-world scenarios. Consider bringing examples of your work, such as code snippets or visualizations, to demonstrate your technical capabilities effectively.

Prepare for Business Impact Discussions

10X Genomics values candidates who can translate data analysis into actionable business insights. Be ready to discuss how your previous work has contributed to business outcomes, such as increasing campaign effectiveness or optimizing marketing strategies. Frame your experiences in terms of measurable results, such as improved customer acquisition costs (CAC) or lifetime value (LTV).

Understand the Company’s Mission and Values

Familiarize yourself with 10X Genomics' mission to advance human health through biological mastery. Reflect on how your personal values align with this mission and be prepared to articulate your passion for contributing to meaningful scientific advancements. This alignment will resonate well with interviewers and demonstrate your commitment to the company’s goals.

Highlight Your Problem-Solving Skills

The ability to tackle complex problems is crucial in this role. Prepare to discuss specific challenges you have faced in your previous positions and the innovative solutions you implemented. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your thought process and the impact of your solutions.

Be Ready to Discuss Data Visualization

Effective communication of data insights is key at 10X Genomics. Familiarize yourself with data visualization tools like Tableau or PowerBI, and be prepared to discuss how you have used these tools to present data in a clear and impactful manner. Consider sharing examples of dashboards or reports you have created that led to significant business decisions.

Cultivate a Growth Mindset

10X Genomics encourages its employees to pursue new ideas and follow their passions. During the interview, express your enthusiasm for continuous learning and professional development. Share instances where you have sought out new knowledge or skills, particularly in areas relevant to the role, such as generative AI or advanced analytics.

Prepare for Behavioral Questions

Expect behavioral questions that assess your adaptability, teamwork, and communication skills. Reflect on past experiences that demonstrate your ability to thrive in a fast-paced environment and manage multiple priorities. Use specific examples to illustrate your resilience and ability to deliver results under pressure.

Ask Insightful Questions

Prepare thoughtful questions that reflect your understanding of the company and the role. Inquire about the team dynamics, current projects, or how success is measured in the Data Scientist position. This not only shows your interest in the role but also helps you gauge if the company culture aligns with your expectations.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at 10X Genomics. Good luck!

10X Genomics Data Scientist Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at 10X Genomics. The interview will focus on your analytical skills, experience with data manipulation, and ability to derive actionable insights from complex datasets. Be prepared to discuss your technical expertise in machine learning, statistics, and data visualization, as well as your experience in marketing analytics.

Machine Learning

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

Understanding the distinction between these two types of learning is fundamental in data science, especially when discussing model selection and application.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category. Discuss scenarios where one might be preferred over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as regression and classification tasks. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like clustering algorithms. For instance, I used supervised learning to predict customer churn based on historical data, while I applied unsupervised learning to segment customers into distinct groups for targeted marketing.”

2. Describe a machine learning project you have worked on. What was your role?

This question assesses your practical experience and ability to contribute to projects.

How to Answer

Outline the project’s objective, your specific contributions, and the outcomes. Highlight any challenges faced and how you overcame them.

Example

“I worked on a project to develop a predictive model for customer lifetime value. My role involved data preprocessing, feature selection, and model training using regression techniques. I collaborated with the marketing team to ensure the model aligned with their strategies, resulting in a 20% increase in targeted campaign effectiveness.”

3. How do you handle overfitting in your models?

Overfitting is a common issue in machine learning, and interviewers want to know your strategies for mitigating it.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Provide examples of how you have applied these methods in past projects.

Example

“To prevent overfitting, I often use cross-validation to ensure my model generalizes well to unseen data. Additionally, I apply regularization techniques like Lasso or Ridge regression to penalize overly complex models. In a recent project, I noticed overfitting in my initial model, so I implemented these strategies, which improved the model’s performance on validation data.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation and the importance of selecting appropriate metrics.

How to Answer

Mention various metrics relevant to the type of model (e.g., accuracy, precision, recall, F1 score for classification; RMSE, MAE for regression) and explain when to use each.

Example

“I typically use accuracy and F1 score for classification models to balance precision and recall. For regression tasks, I prefer RMSE as it gives a clear indication of the model’s prediction error. In a recent classification project, I found that while accuracy was high, the F1 score revealed issues with class imbalance, prompting me to adjust my approach.”

Statistics & Probability

1. Explain the concept of p-value and its significance in hypothesis testing.

Understanding p-values is crucial for interpreting statistical results and making data-driven decisions.

How to Answer

Define p-value and explain its role in hypothesis testing, including what it indicates about the null hypothesis.

Example

“A p-value measures the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis. In my previous role, I used p-values to assess the effectiveness of a marketing campaign, which helped us make informed decisions about resource allocation.”

2. What is the Central Limit Theorem, and why is it important?

This question assesses your foundational knowledge in statistics and its application in data analysis.

How to Answer

Explain the theorem and its implications for sampling distributions and inferential statistics.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about a population based on sample data, which I applied in a project analyzing customer feedback to draw conclusions about overall satisfaction.”

3. How do you approach A/B testing?

A/B testing is a common method for evaluating marketing strategies, and interviewers want to know your methodology.

How to Answer

Discuss the design of the experiment, how you determine sample size, and how you analyze the results.

Example

“I start by defining clear hypotheses and metrics for success. I then determine the appropriate sample size using power analysis to ensure statistical significance. After running the test, I analyze the results using statistical methods to determine if the observed differences are significant, which helps guide marketing decisions.”

4. Can you explain the difference between Type I and Type II errors?

Understanding these errors is essential for evaluating the reliability of statistical tests.

How to Answer

Define both types of errors and provide examples of their implications in a business context.

Example

“A Type I error occurs when we incorrectly reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in a marketing campaign analysis, a Type I error might lead us to believe a campaign was effective when it wasn’t, resulting in wasted resources. Conversely, a Type II error could mean missing out on a successful campaign opportunity.”

Data Manipulation & Visualization

1. What tools and libraries do you use for data manipulation and analysis?

This question assesses your technical skills and familiarity with industry-standard tools.

How to Answer

Mention specific tools and libraries you have experience with, and explain how you use them in your workflow.

Example

“I primarily use Python with libraries like Pandas for data manipulation and NumPy for numerical analysis. For visualization, I often utilize Matplotlib and Seaborn to create insightful graphs. In a recent project, I used these tools to clean and analyze a large dataset, which helped identify key trends in customer behavior.”

2. How do you ensure data quality and integrity in your analyses?

Data quality is critical for accurate insights, and interviewers want to know your approach to maintaining it.

How to Answer

Discuss methods for data validation, cleaning, and monitoring throughout the analysis process.

Example

“I ensure data quality by implementing validation checks during data collection and cleaning processes. I regularly assess for missing values, duplicates, and outliers, using techniques like imputation or removal as necessary. This rigorous approach was vital in a recent project where data integrity directly impacted our marketing strategy recommendations.”

3. Describe your experience with data visualization tools. Which do you prefer and why?

This question evaluates your ability to communicate insights effectively through visualization.

How to Answer

Discuss your experience with various tools and your preference based on specific use cases.

Example

“I have experience with Tableau and Power BI for creating interactive dashboards, but I prefer using Python libraries like Plotly for custom visualizations. Plotly allows for greater flexibility and integration with my data analysis workflow, which was particularly beneficial in a project where I needed to present complex data in an easily digestible format.”

4. How do you approach building a dashboard for stakeholders?

This question assesses your ability to translate data into actionable insights for non-technical audiences.

How to Answer

Explain your process for understanding stakeholder needs, selecting relevant metrics, and designing the dashboard.

Example

“I start by meeting with stakeholders to understand their goals and the key metrics they need to track. I then design the dashboard to highlight these metrics clearly, using visualizations that make the data easy to interpret. In a recent project, I created a dashboard for the marketing team that provided real-time insights into campaign performance, which significantly improved their decision-making process.”

• 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.

• 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.

• 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.

• Write a function to get a sample from a standard normal distribution. Create a function to generate a sample from a standard normal distribution.

• Write an efficient function nearest_entries to find the closest element to N in a sorted list and return surrounding 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 finds the element closest to N, returning that element along with the k-next and k-previous elements of the list.

• How would you analyze the churn behavior of users on different Netflix pricing plans? Netflix has two pricing plans: $15/month or $100/year. An executive wants to understand the churn behavior of users on these plans. What metrics, graphs, and models would you build to provide an overarching view of subscription performance?

• How would you predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, you need to build a model to predict which merchants the company should target for acquisition when entering a new market. How would you approach this task?

• 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?

• How would you measure and address the success of LinkedIn’s newsfeed ranking algorithm?

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

• If some success metrics for the newsfeed algorithm are increasing while others are decreasing, how would you approach this situation?

• How would you determine the statistical significance of an AB test for a landing page redesign? You want to launch a redesign of a landing page to improve the click-through rate using an AB test. How would you infer if the results of the click-through rate were statistically significant or not?

• 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 a non-technical person. Focus on its role in hypothesis testing and what it indicates about the results.

• 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.

• How would you determine if the results of an AB test on click-through rate 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 interpretation.

• 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 develop a model to identify which merchants the company should target for acquisition when entering a new market?

• How would you assign point values to letters in a Spanish Scrabble game 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?

Embarking on a journey to secure the Data Scientist position at 10X Genomics is a promising adventure that blends cutting-edge science with data expertise. For more detailed insights and tailored preparation tips, check out our comprehensive 10X Genomics Interview Guide. We’ve also crafted guides for other roles like software engineer and data analyst, helping you understand the unique nuances of 10X Genomics' interview process across different positions.

At Interview Query, we equip you with a wealth of resources, empowering you with the knowledge, confidence, and strategic insights needed to excel in your interview journey. Explore all our company interview guides for a well-rounded preparation experience, and feel free to reach out if you have any questions.

Good luck with your interview, and get ready to make your mark at 10X Genomics!