Ancestry Data Scientist Interview Questions + Guide in 2025

Ancestry is the global leader in family history, dedicated to empowering individuals on their journeys of personal discovery through a vast collection of records and advanced genetic insights.

As a Data Scientist at Ancestry, you will play a crucial role in analyzing complex biological and genomic datasets to uncover insights that enrich the lives of customers. Your key responsibilities will include designing and implementing advanced machine learning algorithms, collaborating with cross-functional teams including geneticists and engineers, and utilizing cloud-based tools such as AWS for data processing and analysis. A strong background in computational biology, proficiency in Python and AWS, and a deep understanding of genetics and genomics are essential to excel in this role. Ancestry values a diverse and inclusive work environment, making it vital for candidates to demonstrate not only technical expertise but also strong communication skills and a collaborative spirit.

This guide will equip you with tailored insights and preparation strategies to confidently navigate your interview with Ancestry, ensuring you highlight your strengths and align with the company's values and mission.

The interview process for a Data Scientist role at Ancestry is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different competencies relevant to the role.

1. Initial HR Screening

The process begins with a 30-minute phone interview with a recruiter. This initial screening focuses on understanding your background, motivations for applying, and alignment with Ancestry's values. The recruiter will also discuss the role's expectations and the company culture, providing you with insights into what it’s like to work at Ancestry.

2. Technical Assessment

Following the HR screening, candidates usually undergo a technical assessment, which may be conducted via a coding platform like HackerRank. This round typically lasts around 30 minutes and focuses on your coding skills, particularly in Python. You may be asked to solve medium-level programming problems, which could include dynamic programming or string manipulation tasks.

3. Machine Learning Interview

The next step often involves a video interview dedicated to machine learning concepts. This round assesses your understanding of various machine learning models, their applications, and your past experiences in the field. Expect questions that require you to explain algorithms, evaluate model performance metrics like precision and recall, and discuss your approach to solving hypothetical problems using machine learning techniques.

4. Onsite Interviews

If you progress past the initial rounds, you will be invited for onsite interviews, which typically consist of multiple rounds (often five) with different team members. Each interview lasts about 45 minutes and covers a range of topics, including advanced machine learning techniques, data analysis, and coding challenges. You may also be asked to discuss your previous research and how it relates to the work at Ancestry. There is usually a break after the third round to help you recharge.

5. Final Technical and Behavioral Round

The final round may include a mix of technical and behavioral questions, where you will need to demonstrate your problem-solving skills and ability to work collaboratively in a multidisciplinary team. This round is crucial for assessing how well you can communicate complex data insights to a diverse audience and your fit within Ancestry's inclusive culture.

As you prepare for these interviews, it's essential to be ready for a variety of questions that will test your technical knowledge and interpersonal skills.

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

Understand the Role's Technical Requirements

As a Data Scientist at Ancestry, you will be expected to have a strong grasp of machine learning algorithms, particularly in the context of genomic data. Brush up on your knowledge of decision trees, random forests, and ensemble methods, as these are frequently discussed in interviews. Be prepared to explain the pros and cons of different models and how they apply to real-world scenarios, especially in computational biology.

Prepare for Coding Challenges

Expect to face coding challenges that test your proficiency in Python and SQL. Familiarize yourself with platforms like HackerRank, as many candidates have reported coding assessments conducted there. Practice medium-level coding problems, particularly those related to dynamic programming and string manipulation, as these are common themes in the interview process. Make sure you can articulate your thought process while solving problems, as interviewers appreciate candidates who can communicate their reasoning clearly.

Highlight Relevant Experience

Your past experiences, especially those related to machine learning and data analysis in a biological context, will be crucial. Be ready to discuss specific projects you've worked on, the datasets you handled, and the impact of your work. Tailor your responses to demonstrate how your background aligns with Ancestry's mission of enriching lives through data. This will not only showcase your qualifications but also your passion for the role.

Emphasize Collaboration and Communication Skills

Ancestry values teamwork and communication, particularly in a multidisciplinary environment. Be prepared to discuss how you've collaborated with others in previous roles, especially with population geneticists or engineers. Highlight your ability to present complex data findings to diverse audiences, as this is essential for effectively conveying insights derived from your analyses.

Familiarize Yourself with Ancestry's Culture

Ancestry prides itself on being a human-centered company that values diversity and inclusion. Research the company's values and recent initiatives to understand its culture better. During the interview, express your alignment with these values and how you can contribute to fostering an inclusive work environment. This will demonstrate that you are not only a fit for the role but also for the company as a whole.

Stay Current with Industry Trends

Given the fast-paced nature of computational biology and machine learning, staying updated on the latest research and advancements is crucial. Be prepared to discuss recent developments in genomics and how they might impact Ancestry's work. This will show your commitment to continuous learning and your enthusiasm for contributing to the company's innovative projects.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Scientist role at Ancestry. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Ancestry. The interview process will likely focus on your technical skills in machine learning, data analysis, and programming, as well as your understanding of genetics and bioinformatics. Be prepared to discuss your past experiences and how they relate to the role, as well as to solve coding problems and answer theoretical questions.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key differences, including the presence of labeled data in supervised learning and the absence of labels in unsupervised learning. Provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as classification tasks using decision trees. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering algorithms such as K-means.”

2. Describe ensemble learning and its advantages.

Ensemble methods are important in improving model performance. Be prepared to discuss various ensemble techniques.

How to Answer

Explain what ensemble learning is and mention popular methods like bagging and boosting. Highlight the benefits of using ensemble methods.

Example

“Ensemble learning combines multiple models to improve overall performance. Techniques like Random Forests use bagging to reduce variance, while boosting methods like AdaBoost focus on correcting errors of weak learners, leading to a more robust model.”

3. How do you handle overfitting in machine learning models?

Overfitting is a common issue in model training. Discuss strategies to mitigate it.

How to Answer

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

Example

“To combat overfitting, I often use cross-validation to ensure my model generalizes well to unseen data. Additionally, I apply L1 or L2 regularization to penalize overly complex models, which helps maintain a balance between bias and variance.”

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

Understanding model evaluation is key in data science. Be ready to discuss various metrics.

How to Answer

Discuss metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Explain when to use each metric based on the context of the problem.

Example

“I typically evaluate classification models using precision and recall, especially in imbalanced datasets. The F1-score provides a balance between the two, while ROC-AUC gives insight into the model's performance across different thresholds.”

5. Can you explain how you would approach a regression problem?

Regression analysis is a fundamental skill for data scientists. Be prepared to outline your approach.

How to Answer

Discuss the steps you would take, from data exploration to model selection and evaluation. Mention specific algorithms you would consider.

Example

“I would start by exploring the data to understand relationships and distributions. After preprocessing, I might use linear regression for its simplicity or more complex models like Random Forest Regressor if non-linearity is present. Finally, I would evaluate the model using RMSE and R-squared metrics.”

Statistics & Probability

1. How do you determine if a dataset is normally distributed?

Statistical understanding is essential for data analysis. Be prepared to discuss methods for assessing normality.

How to Answer

Mention visual methods like histograms and Q-Q plots, as well as statistical tests like the Shapiro-Wilk test.

Example

“I assess normality by visualizing the data with histograms and Q-Q plots. Additionally, I apply the Shapiro-Wilk test to statistically confirm if the dataset deviates from a normal distribution.”

2. Explain the concept of p-value in hypothesis testing.

Understanding hypothesis testing is crucial in data science. Be ready to explain p-values clearly.

How to Answer

Define p-value and its significance in hypothesis testing, and discuss how it helps in decision-making.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

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

The Central Limit Theorem is a key concept in statistics. Be prepared to explain its implications.

How to Answer

Discuss the theorem's statement and its significance in making inferences about population parameters.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences and conducting hypothesis tests.”

4. How do you handle missing data in a dataset?

Handling missing data is a common challenge. Be ready to discuss your strategies.

How to Answer

Mention techniques such as imputation, deletion, or using algorithms that support missing values. Provide examples of when you used these methods.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I might use mean imputation for small amounts of missing data or consider more sophisticated methods like KNN imputation for larger gaps.”

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

Understanding errors in hypothesis testing is essential. Be prepared to define and differentiate them.

How to Answer

Clearly define both types of errors and provide examples to illustrate their implications.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in a medical test, a Type I error could mean falsely diagnosing a disease, while a Type II error could mean missing a diagnosis when the disease is present.”

Coding and Data Manipulation

1. Describe your experience with Python for data analysis.

Python is a key tool for data scientists. Be prepared to discuss your proficiency and experience.

How to Answer

Highlight specific libraries you have used, such as Pandas, NumPy, and Scikit-learn, and provide examples of projects where you applied them.

Example

“I have extensive experience using Python for data analysis, particularly with Pandas for data manipulation and NumPy for numerical computations. In my last project, I used these libraries to clean and analyze a large genomic dataset, which led to significant insights.”

2. How do you optimize SQL queries for performance?

SQL skills are essential for data manipulation. Be ready to discuss optimization techniques.

How to Answer

Mention strategies such as indexing, avoiding SELECT *, and using JOINs efficiently.

Example

“To optimize SQL queries, I focus on indexing key columns to speed up searches and avoid using SELECT * to reduce data transfer. I also ensure that JOINs are used judiciously to minimize complexity and improve performance.”

3. Can you explain how you would implement an ETL process?

ETL processes are crucial for data management. Be prepared to outline your approach.

How to Answer

Discuss the steps involved in Extracting, Transforming, and Loading data, and mention any tools you have used.

Example

“I would start by extracting data from various sources, then transform it by cleaning and structuring it for analysis. Finally, I would load the processed data into a data warehouse, using tools like AWS Glue for automation and efficiency.”

4. Describe a challenging coding problem you solved.

Be prepared to discuss a specific coding challenge and your approach to solving it.

How to Answer

Outline the problem, your thought process, and the solution you implemented.

Example

“I faced a challenge in optimizing a data processing script that was running too slowly. I analyzed the code and identified bottlenecks, then refactored it to use more efficient algorithms and parallel processing, which reduced the runtime significantly.”

5. How do you ensure the quality of your data?

Data quality is critical in data science. Be ready to discuss your methods for ensuring data integrity.

How to Answer

Mention techniques such as validation checks, data cleaning, and regular audits.

Example

“I ensure data quality by implementing validation checks during data entry, performing regular audits to identify inconsistencies, and using data cleaning techniques to rectify any issues before analysis.”