Guardian Life Data Scientist Position Overview

Guardian Life is a forward-thinking insurance company committed to enhancing the well-being of its customers and their families. Recently, Guardian has embarked on a transformation journey under the leadership of a newly appointed Chief Data & Analytics Officer to guide the Enterprise Data and Analytic Office (EDAO).

As a Data Scientist at Guardian Life, you will be at the forefront of leveraging machine learning and artificial intelligence to drive enterprise-wide innovation. The role offers a unique opportunity to contribute to Guardian's Data Science Lab, developing advanced data science solutions and collaborating with cross-functional teams on high-impact projects. The ideal candidate should have a strong background in data science, particularly with Large Language Models (LLM), and be passionate about implementing cutting-edge technologies to solve complex business problems.

Ready to take the next step in your career with Guardian Life? Dive into our comprehensive interview guide on Interview Query.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Guardian Life as a Data Scientist. Whether you were contacted by a Guardian Life recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Guardian Life Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Guardian Life Data Scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Take-Home Assessment

Successfully navigating the recruiter round will present you with an invitation for a technical take-home assessment. This assessment is primarily focused on Natural Language Processing (NLP) and text processing. You will be evaluated on your ability to handle unstructured data and create effective machine learning solutions.

Technical Virtual Interview

The next step is a technical virtual interview that will involve a more in-depth analysis of your technical skills. This 1-hour long interview will focus on your past ML projects and experience with SQL and Python, especially Python Pandas. You may need to discuss your previous projects and how they relate to the requirements of the Data Scientist role at Guardian Life.

This round may also assess your ability to review code and demonstrate technical skills through problem-solving tasks.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Guardian Life office. Your technical prowess, including programming, ML modeling capabilities, and your expertise in Large Language Models (LLM) and Generative AI, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Scientist role at Guardian Life.

Quick Tips For Guardian Life Data Scientist Interviews

• Should contain three tips for interviewing for this specific company based on interview experiences.

Example:

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Google interview include:

• Know Guardian Life’s Core Values: Guardian Life emphasizes a commitment to continuous improvement and innovation. Be prepared to discuss how your past experiences align with their mission and values.
• Demonstrate Technological Proficiency: Guardian Life’s Data Scientist interviews assess how well you can leverage AI/ML techniques to generate business-driving insights. Brush up on your knowledge of NLP, LLM, and Python libraries such as PyTorch and TensorFlow.
• Prepare for Project Discussions: Be ready to delve into the details of your past machine learning projects. Focus on your problem-solving skills, the ML algorithms you used, and the impact of your solutions on business objectives.

Typically, interviews at Guardian Life vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function get_ngrams to return a dictionary of n-grams and their frequency in a string. Write a function get_ngrams to take in a word (string) and return a dictionary of n-grams and their frequency in the given string.

• Write a function to determine if a string is a palindrome. Given a string, write a function to determine if it is a palindrome. A palindrome reads the same forwards and backwards.

• Write a query to find users currently "Excited" and never "Bored" with a campaign. Write a query to find all users that are currently "Excited" and have never been "Bored" with a campaign.

• Write a function moving_window to find the moving window average. Given a list of numbers nums and an integer window_size, write a function moving_window to find the moving window average.

• What's the probability that the second card is not an Ace? You have to draw two cards from a shuffled deck, one at a time. Calculate the probability that the second card drawn is not an Ace.

• What are type I and type II errors in hypothesis testing? Explain the difference between type I errors (false positives) and type II errors (false negatives) in hypothesis testing. Bonus: Describe the probability of making each type of error mathematically.

• How much do you expect to pay for a sports game ticket? You can buy a scalped ticket for $50 with a 20% chance of not working. If it doesn't work, you'll need to buy a box office ticket for $70. Calculate the expected cost and how much money you should set aside for the game.

• Is the coin fair if it comes up tails 8 times out of 10 flips? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• What is the difference between covariance and correlation? Explain the difference between covariance and correlation, and provide an example to illustrate the concepts.

• What methods could you use to increase recall in Amazon's product search without changing the search algorithm? As a data scientist at Amazon, you want to improve the search results for product searches but cannot change the underlying logic in the search algorithm. What methods could you use to increase recall?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? You are tasked with building a spam classifier for emails and have built a V1 of the model. What metrics would you use to track the accuracy and validity of the model?

• How would you justify the complexity of a neural network model and explain predictions to non-technical stakeholders? Your manager asks you to build a model with a neural network to solve a business problem. How would you justify the complexity of building such a model and explain the predictions to non-technical stakeholders?

• How would you evaluate and validate a decision tree model for predicting loan repayment? As a data scientist at a bank, you are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate whether using a decision tree algorithm is the correct model for the problem? How would you evaluate the performance of the model before and after deployment?

• When would you use a bagging algorithm versus a boosting algorithm? You are comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? Provide an example of the tradeoffs between the two.

• What are type I and type II errors in hypothesis testing? In hypothesis testing, type I errors (false positives) occur when a true null hypothesis is incorrectly rejected. Type II errors (false negatives) occur when a false null hypothesis is not rejected. Mathematically, the probability of a type I error is denoted by alpha (α), and the probability of a type II error is denoted by beta (β).

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in NYC and Charlotte and needs a process for selecting dashers. How would you decide which Dashers do these deliveries, and would the criteria for selection be the same for both cities?

• How would you improve Google Maps and measure the success of your improvements? As a PM on Google Maps, suggest improvements to the app. Specify the metrics you would check to determine if your feature improvements are successful.

• Why are job applications decreasing while job postings remain the same? You observe that the number of job postings per day has remained constant, but the number of applicants has been decreasing. What could be causing this trend?

• How would you analyze the performance of a new LinkedIn feature without an A/B test? LinkedIn launched a feature allowing candidates to message hiring managers directly during the interview process. Due to engineering constraints, an A/B test wasn't possible. How would you analyze the feature's performance?

What is the average salary for a Data Scientist at Guardian Life?

Q: What does the interview process for the Data Scientist position at Guardian Life involve?

The interview process typically begins with a recruiter reaching out to candidates for initial assessment. This could include a technical take-home assignment focused on NLP and text processing. The second round often involves a detailed discussion of past machine learning projects, as well as an evaluation of technical skills.

Q: What kind of projects will I be working on at Guardian Life as a Data Scientist?

You will work on high-impact projects leveraging advanced machine learning and AI. These could include improving underwriting risk assessment, automating claims adjudication, and enhancing customer servicing using large language models and generative AI capabilities.

Q: What skills are required for the Data Scientist role at Guardian Life?

Candidates should have a solid background in machine learning, deep learning including LLMs, and proficiency in Python and SQL. Hands-on experience with data wrangling, ETL processes, and familiarity with tools such as PyTorch or TensorFlow are highly desirable. Strong communication skills and the ability to work collaboratively across teams are also important.

Q: What is Guardian Life's approach to data science?

Guardian Life has established a Data Science Lab (DSL) to drive innovation via data-driven decision-making. The focus is on using emerging technologies like AI and machine learning to develop solutions that enhance the company’s products and services. This lab fosters an environment for rapid testing and implementation of new technologies.

Q: What benefits does Guardian Life offer to Data Scientists?

Guardian Life offers a comprehensive benefits package including flexible work arrangements, unlimited paid time off for most roles, medical, dental, and vision plans, as well as life and disability insurance. Employee wellness programs, retirement plans with a company match, and opportunities for skill-building and career growth are also provided.

In conclusion, Guardian Life stands out as a company deeply invested in leveraging data science to drive innovation and growth. With recent additions to leadership, including a Chief Data & Analytics Officer, and the establishment of a forward-thinking Data Science Lab, Guardian is committed to transforming into a modern, data-driven insurance company. For those looking to make an impactful contribution with their data science skills, Guardian offers a dynamic environment where cutting-edge technology and collaboration thrive.

If you want more insights about the company, check out our main Guardian Life 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 Guardian Life’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 Guardian Life 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!