Lincoln Financial Group Data Scientist Interview Questions + Guide in 2025

Lincoln Financial Group specializes in helping individuals plan, protect, and retire with confidence, serving approximately 16 million customers across various financial products and services.

The Data Scientist role at Lincoln Financial Group is pivotal in driving data-driven decision-making across multiple business units, including operations, customer service, underwriting, and distribution. This position involves the end-to-end execution of data science projects, from identifying business challenges to developing and deploying machine learning models in a production environment. Key responsibilities include collaborating with cross-functional teams to design and implement Machine Learning Operations (MLOps) frameworks, ensuring the secure handling of data, and providing subject matter expertise to enhance organizational initiatives. Ideal candidates will possess strong statistical skills, proficiency in Python, and a deep understanding of machine learning algorithms, along with the ability to communicate complex technical concepts to non-technical stakeholders. A passion for problem-solving and a collaborative mindset are essential traits for success in this role, particularly within a company that values diversity and innovation.

This guide will help prepare you for your interview by providing insights into the expectations and competencies that Lincoln Financial Group seeks in a Data Scientist, enabling you to present your qualifications effectively.

The interview process for a Data Scientist at Lincoln Financial Group is structured and thorough, reflecting the company's commitment to finding the right fit for their team. The process typically includes several stages, each designed to assess both technical and interpersonal skills.

1. Initial Screening

The first step in the interview process is an initial screening, which usually takes place over the phone. During this 20-30 minute call, a recruiter will review your resume in detail and discuss your background, skills, and interest in the position. This is also an opportunity for you to learn more about the company culture and the specifics of the role.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview. This may be conducted via video conference and focuses on assessing your technical skills, particularly in areas such as statistics, algorithms, and programming languages like Python. You may be asked to solve coding problems or discuss your experience with machine learning models and data pipelines.

3. Behavioral Interview

After the technical interview, candidates often move on to a behavioral interview. This stage involves meeting with hiring managers or team members to discuss your past experiences, problem-solving abilities, and how you work within a team. Expect questions that explore your approach to collaboration, communication, and handling challenges in a work environment.

4. Case Study Presentation

A unique aspect of the interview process at Lincoln Financial Group is the case study presentation. Candidates are typically required to prepare a business case presentation that demonstrates their analytical skills and ability to apply data science concepts to real-world scenarios. This presentation is followed by a Q&A session where interviewers will probe deeper into your thought process and decision-making.

5. Final Interviews

The final stage usually consists of multiple one-on-one interviews with various team members and stakeholders. These interviews may cover both technical and behavioral aspects, allowing the interviewers to assess your fit within the team and the organization as a whole. This stage may also include discussions about your expectations for the role and how you envision contributing to the company's goals.

As you prepare for your interview, it's essential to be ready for a range of questions that will test your knowledge and experience in data science.

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

Prepare for a Lengthy Process

The interview process at Lincoln Financial Group can be extensive, often involving multiple rounds of interviews, both over the phone and in person. Be ready for a thorough evaluation of your skills and experiences. To prepare, practice articulating your background and how it aligns with the role. Familiarize yourself with the company’s values and how they relate to your professional journey. This will help you convey your fit for the organization effectively.

Master the Technical Skills

As a Data Scientist, you will be expected to demonstrate proficiency in statistics, algorithms, and programming languages, particularly Python. Brush up on your knowledge of machine learning algorithms, including GLMs, random forests, and clustering techniques. Be prepared to discuss your experience with MLOps and CI/CD pipelines, as well as your ability to work with SQL and cloud technologies. Practicing coding challenges and technical questions related to these areas will give you a competitive edge.

Communicate Effectively

Strong communication skills are crucial for this role, as you will need to convey complex technical concepts to non-technical stakeholders. Practice explaining your past projects and the impact they had on the business in a clear and concise manner. Be ready to discuss how you can bridge the gap between technical and non-technical teams, showcasing your ability to collaborate cross-functionally.

Showcase Your Problem-Solving Skills

Demonstrate your passion for problem-solving by discussing specific challenges you’ve faced in previous roles and how you overcame them. Highlight your ability to think critically and iteratively, especially in fast-paced environments. Be prepared to share examples of how you’ve developed and deployed machine learning models, emphasizing your role in the end-to-end project lifecycle.

Embrace Feedback and Continuous Learning

Lincoln Financial Group values a culture of learning and collaboration. Be open about your willingness to accept feedback and how you’ve used it to improve your work. Discuss any experiences where you’ve mentored others or contributed to team learning, as this aligns with the company’s emphasis on knowledge sharing.

Prepare for Behavioral Questions

Expect to encounter behavioral questions that assess your fit within the company culture. Reflect on your past experiences and be ready to discuss how you’ve handled various situations, such as conflict resolution, teamwork, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and relevant examples.

Understand the Company’s Mission

Familiarize yourself with Lincoln Financial Group’s mission and values. Be prepared to articulate why you want to work for the company and how your personal values align with theirs. This will not only demonstrate your interest in the role but also your commitment to contributing positively to the organization.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Lincoln Financial Group. The interview process will likely focus on your technical skills, problem-solving abilities, and your experience in data science applications, particularly in a business context. Be prepared to discuss your past projects, methodologies, and how you can contribute to the company's goals.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role, as you will be expected to apply these techniques in real-world scenarios.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each method is best suited for.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, like customer segmentation in marketing.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills in machine learning.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them. Emphasize your contributions and the impact of the project.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset and improved our model's accuracy by 15%.”

3. How do you evaluate the performance of a machine learning model?

Evaluating model performance is critical in ensuring the effectiveness of your solutions.

How to Answer

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

Example

“I evaluate model performance using multiple metrics. For classification tasks, I often look at accuracy and F1 score to balance precision and recall, especially in cases where false positives and negatives have different costs.”

4. What is overfitting, and how can you prevent it?

Understanding overfitting is essential for building robust models.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation to ensure the model generalizes well and apply regularization methods to penalize overly complex models.”

5. Can you explain the concept of feature engineering?

Feature engineering is a key part of the data preparation process.

How to Answer

Discuss the importance of selecting and transforming variables to improve model performance, and provide examples of techniques you have used.

Example

“Feature engineering involves creating new features or modifying existing ones to enhance model performance. For instance, in a sales prediction model, I created a feature for the time of year to capture seasonal trends, which significantly improved our predictions.”

Statistics & Probability

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

This question tests your understanding of statistical principles that underpin data analysis.

How to Answer

Explain the Central Limit 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 population parameters based on sample statistics.”

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

Handling missing data is a common challenge in data science.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, deletion, or using algorithms that support missing values.

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 if the missingness is significant.”

3. Explain the difference between Type I and Type II errors.

Understanding these errors is vital for hypothesis testing.

How to Answer

Define both types of errors and provide examples of their implications in decision-making.

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 trial, a Type I error could mean falsely concluding a drug is effective, while a Type II error could mean missing out on a beneficial treatment.”

4. What is a p-value, and how do you interpret it?

P-values are fundamental in hypothesis testing.

How to Answer

Define a p-value and explain its significance in determining statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

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

Normality is an important assumption in many statistical tests.

How to Answer

Discuss methods for assessing normality, such as visual inspections (histograms, Q-Q plots) and statistical tests (Shapiro-Wilk test).

Example

“To determine if a dataset is normally distributed, I use visual methods like histograms and Q-Q plots to check for symmetry and bell-shaped curves. Additionally, I apply the Shapiro-Wilk test to statistically assess normality.”

Programming & Technical Skills

1. How would you append to an array in Python?

This question tests your programming knowledge, particularly in Python.

How to Answer

Explain the method you would use to append an element to an array, mentioning relevant libraries if applicable.

Example

“To append to an array in Python, I would use the append() method if using a list. For example, my_list.append(new_element) adds new_element to the end of my_list.”

2. Can you explain the concept of MLOps?

MLOps is increasingly important in deploying machine learning models.

How to Answer

Define MLOps and discuss its significance in the machine learning lifecycle.

Example

“MLOps, or Machine Learning Operations, is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. It combines machine learning, DevOps, and data engineering to streamline the model lifecycle from development to deployment and monitoring.”

3. What is your experience with SQL and how do you use it in data analysis?

SQL skills are essential for data manipulation and retrieval.

How to Answer

Discuss your experience with SQL, including specific functions or queries you commonly use.

Example

“I have extensive experience with SQL for data analysis, using it to extract and manipulate data from relational databases. I often use JOINs to combine tables, GROUP BY for aggregations, and window functions for advanced analytics.”

4. Describe your experience with cloud technologies, particularly AWS.

Cloud technologies are crucial for modern data science applications.

How to Answer

Outline your experience with cloud platforms, focusing on specific services you have used.

Example

“I have worked extensively with AWS, utilizing services like S3 for data storage, EC2 for computing resources, and SageMaker for building and deploying machine learning models. This experience has allowed me to scale my projects effectively.”

5. How do you ensure code quality and maintainability in your projects?

Code quality is vital for collaborative work and long-term project success.

How to Answer

Discuss practices you follow to maintain high code quality, such as code reviews, testing, and documentation.

Example

“I ensure code quality by adhering to best practices like writing unit tests, conducting code reviews with peers, and maintaining clear documentation. This approach not only improves code maintainability but also facilitates collaboration within the team.”