Headquartered in Salt Lake City, Utah, SILAC Insurance Company is one of the fastest-growing insurance companies in the nation. SILAC offers industry-leading annuity products that help individuals prepare for retirement, with a vision centered on providing clean, simple, and competitive products. With a dedicated team of over 300 employees, SILAC prides itself on adaptability, innovation, and trustworthiness, guided by core values such as Transparency & Trust, and Teamwork & Collaboration.

Becoming a Data Analyst at SILAC involves working with SQL, building reports, and handling complex data sets. Candidates require advanced SQL skills, experience in tools like SSRS, Tableau, Power BI, data analytics experience, and strong communication and problem-solving abilities. This role offers hybrid flexibility, a comprehensive benefits package, and an inclusive work environment committed to diversity.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining SILAC Insurance Company as a Business Intelligence Data Analyst. Whether you were contacted by a SILAC 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 SILAC 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 SILAC data analyst 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 Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the SILAC data analyst role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around SILAC’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

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 SILAC office. Your technical prowess, including programming and ML modeling capabilities, 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 analyst role at SILAC.

Quick Tips For SILAC Insurance Company Data Analyst 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 SILAC Insurance Company interview include:

• Know Your SILAC Products: SILAC questions are standardized and rely heavily on situational scenarios with their products. Study SILAC’s large breadth of products and understand how you would personally improve or test them.
• Be Data Driven: SILAC’s data science interviews assess how well you can provide business-driving insights with data analysis. Brush up on your knowledge of statistics and probability, given these questions can be some of the hardest to solve.
• Embody the Core Values: SILAC at its core has a collaborative, employee-focused culture that values innovation and integrity. Practice responding to behavioral questions with answers that touch on SILAC’s core values, including Adaptability, Transparency, and Teamwork.

Typically, interviews at Silac Insurance Company vary by role and team, but commonly Data Analyst interviews follow a fairly standardized process across these question topics.

• 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, while 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? To decide which Dashers should do deliveries in NYC and Charlotte, consider factors like past performance, customer ratings, and availability. Evaluate if the criteria should differ based on city-specific factors such as traffic patterns, delivery volume, and local regulations.

• How would you improve Google Maps and measure success? To improve Google Maps, identify user pain points and add features like real-time traffic updates or enhanced public transit information. Measure success using metrics such as user engagement, feature usage rates, and user satisfaction scores.

• Why are job applications decreasing despite steady job postings? Investigate potential reasons for the decrease in job applications, such as changes in the job market, user experience issues on the job board, or increased competition from other job platforms.

• How would you analyze the performance of LinkedIn's new feature for messaging hiring managers? To analyze the new feature's performance, track metrics like the number of messages sent, response rates from hiring managers, user satisfaction, and any changes in the time to hire. Compare these metrics to historical data to assess the feature's impact.

• 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.

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

• What methods could you use to increase recall in product search results 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 model? 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 its 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? If you move forward with the decision tree model, how would you evaluate its performance 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's the probability that the second card drawn from a shuffled deck 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? In the context of hypothesis testing, explain type I errors (false positives) and type II errors (false negatives). Describe the difference between the two and, if possible, provide the mathematical probability of making each type of error.

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

• Is a coin that lands tails 8 out of 10 times fair? 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 distinction.

What is the average salary for a Data Analyst at Silac Insurance Company?

Q: What does SILAC Insurance Company specialize in? SILAC Insurance Company is one of the fastest-growing insurance companies in the nation, headquartered in Salt Lake City, Utah. Known for providing industry-leading annuity products, SILAC helps individuals prepare for retirement while delivering best-in-class service to agents, business partners, and clients.

Q: What are the core values of SILAC Insurance Company? At SILAC, we adhere to the core values of Adaptability & Continuous Improvement, Transparency & Trust, Honesty & Integrity, Teamwork & Collaboration, and Gratitude & Compassion. These values are integral to our everyday operations and corporate culture.

Q: What is the work environment like for a Data Analyst at SILAC Insurance Company? The work environment for a Data Analyst at SILAC is dynamic and inclusive. We celebrate diversity and support varying backgrounds, beliefs, and perspectives. The role allows for hybrid flexibility, typically requiring 40 hours per week, Monday through Friday.

Q: What skills and experience are required for the Data Analyst position? Candidates need 2-3 years of advanced SQL knowledge, including the use of complex functions, temp tables, and database design concepts. Experience with SSIS, report-building tools like SSRS, Tableau, or Power BI, and advanced proficiency in MS Office applications are also essential.

Q: How can I prepare for an interview for the Data Analyst role at SILAC Insurance Company? Preparation should include familiarizing yourself with SILAC's products and values. Prioritize brushing up on your SQL expertise, data analytics experience, and report-building skills. Practicing with platforms like Interview Query can be an invaluable resource to hone your interview skills and technical knowledge.

At SILAC Insurance Company, we pride ourselves on maintaining a culture of adaptability, innovation, and trustworthiness—all while providing industry-leading annuity products and fostering the success of our employees. Our fast-growing and award-winning team, headquartered in Salt Lake City, Utah, is committed to inclusivity and diversity. As a Business Intelligence Data Analyst, you'll join a collaborative environment where your skills in SQL, SSIS, and data analysis will be instrumental in supporting our operations and delivering top-notch service. Want to dive deeper into what to expect from an interview with us? Check out our main SILAC Insurance Company Interview Guide. At Interview Query, we provide the knowledge, confidence, and strategic insights to help you ace your interviews. Explore all our comprehensive company interview guides for better preparation. Good luck with your interview!