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Sagesure insurance managers Interview Questions

sagesure has been helping insurance representatives and policyholders find the right insurance solutions since 2006. when we say we provide “innovative property solutions,” what we mean is that we have a knack for problem solving – and a passion for providing the competitive products our customers need, backed by the service they deserve.

sagesure has been helping insurance representatives and policyholders find the right insurance solutions since 2006. when we say we provide “innovative property solutions,” what we mean is that we have a knack for problem solving – and a passion for providing the competitive products our customers need, backed by the service they deserve.

Overview

Sagesure insurance managers Interview Guides

A/B Testing

Experiment Validity

Delivery Estimate Model

A/B Test Power Size

Green Dot

Delivery Online

Product Sense & Metrics

Group Success

Instagram TV Success

Measure Facebook Stories success by tracking reach, engagement, and actions aligned with specific business goals

Facebook Story Success

Machine Learning

Spam Classifier 

Random Forest Explanation

Xgboost vs Random Forest

Analytics

How would you find out if an increase in user conversion rates after a new email journey is casual or just part of a wider trend?

Causal Email Journey 

How would you analyze how the feature is performing?

Recruiting Leads

Brainteasers

Three Zebras

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Challenge

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

How would you explain what a p-value is to someone who is not technical?

What does a p-value in a statistical test represent?

What does a p-value in a statistical test represent?

P-value to a Layman I

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

P-value to a Layman

Statistics

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

What happens when you average the output on multiple decision trees?

What happens when you average the output on multiple decision trees?

Average Trees

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Given the <code>employees</code> and <code>departments</code> table, write a query to get the top 3 highest employee salaries by department. If the department contains less that 3 employees, the top 2 or the top 1 highest salaries should be listed (assume that each department has at least 1 employee). 

Note: The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order. 

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>employee_nam</code>e</td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Get the top 3 highest employee salaries by department

Top Three Salaries

Let’s say you’re analyzing an AB test that has both a test group and a control group.

<ol>
<li>How do you calculate the sample size necessary for an accurate measurement?</li>

<li>Let’s say that the sample size is similar and sufficient between the two groups. In order to measure very small differences between the two, should the power get bigger or smaller?</li>
</ol>

Let’s say we want to build a new delivery time estimate model for consumers ordering food delivery.

How would you determine if the new model predicts delivery times better than the old model?

When rolling out a new delivery time estimate model in production, what is an important consideration?

When rolling out a new delivery time estimate model in production, what is an important consideration?

Delivery Estimate Model I

What is one way to measure the performance of a new delivery time estimate model for food delivery?

What is one way to measure the performance of a new delivery time estimate model for food delivery?

Delivery Estimate Model II

How would you measure the success of Facebook Groups?

Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

Let’s say you’re a Product Data Scientist at Instagram. How would you measure the success of the Instagram TV product?

What is the primary goal of Instagram TV as a product feature?

What is the primary goal of Instagram TV as a product feature?

Let’s say you are tasked with building a spam classifier for emails. Assume that you’ve built a V1 of the model.

What metrics would you use to track accuracy and validity of the model?

Let’s say you’re a data scientist at LinkedIn where you’re working on a product that sends qualified job candidates to companies. The team has launched a new feature that allows candidates to message hiring managers at companies directly during the interview process to get updates on their status.

Due to engineering constraints, the company can’t AB test the feature before launching it.

How would you analyze how the feature is performing?

Three zebras are chilling in the desert. Suddenly a lion attacks.
Each zebra is sitting on a corner of a triangle with sides of equal length.
Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?

Three zebras are chilling in the desert. Suddenly a lion attacks.

Each zebra is sitting on a corner of a triangle with side of equal length. Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?

Three zebras are chilling in the desert. Suddenly a lion attacks.

Each zebra is sitting on a corner of a triangle with side of equal length.  Each zebra randomly picks a direction and only runs along the outline of the triangle to either edge of the triangle.

What is the probability that none of the zebras collide?



Let’s say we’re working on a new feature for LinkedIn chat. We want to implement a green dot to show an “active user” but given engineering constraints, we can’t AB test it before release.

How would you analyze the effectiveness of this new feature?

Let’s say we want to build a new algorithm for Lyft Line.

How should we test it, measure success of the test, and eventually roll it out?

Building Lyft Line

Write a function <code>data_stream_median</code> to calculate the new median from a stream of ordered integers and the new element which is not yet inserted into the stream.

The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value, so the median is the mean of the two middle values.

Example:

<pre tabindex="0" class="chroma"><code>new_value = 2
stream = [1, 2, 3, 4, 5, 6]
def data_stream_median(new_value, stream): -&gt; 3
</code></pre>

Write a function data_stream_median to calculate the median from a stream of integers.

Data Stream Median

Data Structures & Algorithms

How would you measure success for Facebook Stories?

How would you decide whether or not to create a product, like a job board, for Facebook?

Imagine Facebook is creating a job board. How would you design an A/B test to evaluate its effectiveness?

Assessing the market potential and then use A/B testing to measure its effectiveness against user behavior

Facebook Job Board Design

Let’s say we’re building a new feature that tells dashers when they should go online or not in order to meet delivery demand. 

How would you test whether the feature was working successfully?

Let’s say that you work for an E-commerce store. A new marketing manager joins the company and redesigns the existing new-user email journey to try and boost customer conversion rates. A few weeks after launching the new email journey, the new-user to customer conversion rate goes up from 40% to 43%.

Before you can celebrate, the company’s CMO points out a troubling fact. Just a few months prior to the new manager starting the conversion rate already stood at 45%, before slowly dropping down to the “starting” 40%.

How would you investigate if the redesigned email campaign actually led to the increase in the conversion rate, and that the increase wasn’t instead the result of other factors?

What would be an appropriate method to investigate whether the redesigned email campaign led to an increase in the conversion rate of an E-commerce store, in the absence of infrastructure for an AB test?

What would be an appropriate method to investigate whether the redesigned email campaign led to an increase in the conversion rate of an E-commerce store, in the absence of infrastructure for an AB test?

Causal Email Journey

A survey asked 100 respondents two questions:

<ol>
<li>Do you like tea?</li>
<li>Do you like coffee?</li>
</ol>

70% of respondents said they liked coffee, and 80% of the respondents said they liked tea. Only 10% of respondents said they liked neither.

Based on this survey, what are the upper and lower bounds for the proportion of the population that like tea and coffee?

Given that 70% of people like dogs, 60% like cats, and only 10% dislike both, what is the proportion of people who like both dogs and cats?

Given that 70% of people like dogs, 60% like cats, and only 10% dislike both, what is the proportion of people who like both dogs and cats?

Bounds for Coffee and Tea Drinkers I

In a survey with 100 respondents, 80% like reading, 70% enjoy sports, and 10% dislike both. Using a 95% confidence interval, what are the lower and upper bounds of the proportion of respondents who like both reading and sports?

In a survey with 100 respondents, 80% like reading, 70% enjoy sports, and 10% dislike both. Using a 95% confidence interval, what are the lower and upper bounds of the proportion of respondents who like both reading and sports?

Bounds for Coffee and Tea Drinkers II

Find a bound for how many people drink coffee AND tea based on a survey

Bounds for Coffee and Tea Drinkers

Sagesure insurance managers Salaries by Position

<div><h2>Senior Product Manager</h2>If you're looking for the stability of a profitable, growing company with the entrepreneurial spirit of a startup, we're hiring. SageSure, a leader in catastrophe-exposed property insurance, is seeking a Senior Innovation Product Manager. In this role, you will lead the creation of advanced technical systems that enable SageSure to better evaluate and manage home risk, including tools that accurately estimate replacement costs. By deepening our understanding of the homes we insure, we're able to better protect both the properties and the people who live in them. As a key member of our Innovation team, you'll collaborate with software developers, civil engineers, and data scientists to design cutting-edge tools that assess risk, deliver meaningful insights to homeowners, and drive actions that make homes more resilient to disasters. This role offers the opportunity to manage complex, high-impact products while shaping how data and technology keep communities safer.Partner cross-functionally with software, data science, and underwriting teams to design and deliver systems that enhance our understanding of insured properties.Lead the evolution of tools that estimate replacement costs, ensuring accuracy, scalability, and operational efficiency.Establish new software systems that leverage property data to help homeowners take proactive steps to protect their homes.Own a product roadmap, prioritizing initiatives based on business impact and cross-functional input.Research, evaluate, and recommend emerging technologies and partnerships that strengthen home resilience and enhance the company's risk assessment capabilities.Present program performance and strategic recommendations to leaders across the organization.Drive complex data analysis initiatives and support high-priority projects that advance company objectives and product performance.We're looking for someone who has a Bachelor's degree or higher in a technical field such as engineering, mathematics, data science, or related discipline. Fiveseven years of experience in product management, product development, innovation management, or related field. Exceptional communication skills with the ability to translate complex technical concepts into clear insights for diverse audiences. A data-driven, analytical mindset with proven experience working with large, complex datasets. Proficiency with data analysis and visualization tools such as Python, SQL, Excel, and Power BI (or similar). Familiarity with software development practices (Agile/Scrum) and experience collaborating closely with engineering teams. Experience managing complex cross-functional initiatives, with a proven ability to coordinate stakeholders and meet deadlines. Comfortable navigating ambiguous problems, with a talent for developing creative, practical solutions.Highly preferred candidates also have a Master's degree in engineering, data science, business, or another relevant field. Demonstrated success in launching new products or solutions from concept to execution, especially within an innovation-focused or entrepreneurial environment. Background in property and casualty insurance, insurtech, or related industries. Hands-on experience or insight into homeowner needs (e.g., through construction, renovation, or home improvement).At SageSure, the Home Services team is reimagining what it means to protect and empower homeowners. We're not just offering insurance, we're delivering smart, proactive solutions that help policyholders maintain their homes, avoid costly damage, and enjoy greater peace of mind. By piloting cutting-edge products, leveraging smart technology, and building powerful partnerships, we're creating a new standard for the homeowner experience. Join us and be part of a fast-growing team that's shaping the future of home services one innovative idea at a time.</div>

Job Summary: 
SageSure is a leader in catastrophe-exposed property insurance, seeking an Associate Business Intelligence Analyst. In this role, you will transform data into insights that drive decisions across service operations, ensuring accurate and actionable reporting while collaborating with stakeholders and enhancing operational dashboards. 
Responsibilities: 
• Maintain and enhance operational dashboards and performance reports for Policy Services. • Partner with business stakeholders to translate reporting needs into clear, data-driven solutions. • Analyze trends, identify data anomalies, and provide recommendations for process improvement. • Support the development of SQL queries, data visualizations, and KPI tracking across service areas. • Collaborate with BI engineers and data governance teams to ensure data integrity and consistency. • Assist with ad hoc data pulls, reporting requests, and business reviews. • Contribute to documentation and process standardization for recurring reports and metrics. • Participate in onboarding and cross-training to support business continuity and knowledge sharing. 
Qualifications: 
Required: 
• Bachelor’s degree in Data Analytics, Information Systems, Statistics, Economics, Computer Science, or a related field. • 0–2 years of experience in a data, reporting, or business analytics role (internships or project experience welcome). • Basic proficiency with SQL and data visualization tools such as Power BI, Tableau, or Looker. • Strong analytical, problem-solving, and critical-thinking skills. • Attention to detail and ability to work with large datasets accurately. • Excellent written and verbal communication skills, with an ability to explain technical information to non-technical audiences. • Strong organizational skills with the ability to manage multiple priorities in a fast-paced environment. 
Preferred: 
• Experience or coursework in data modeling, ETL processes, or database management. • Familiarity with Excel advanced functions (pivot tables, lookups, formulas). • Exposure to Python or R for data analysis. • Understanding of insurance operations, policy administration, or customer service analytics. • Demonstrated interest in building a career in data analytics, business intelligence, or data strategy. 
Company: 
SageSure offers property and casualty insurance solutions tailored for homeowners in regions prone to natural catastrophes. Founded in 2009, the company is headquartered in Jersey City, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

Sagesure insurance managers Interview Questions

Overview

Sagesure insurance managers Interview Guides

Challenge

Sagesure insurance managers Interview Questions

Sagesure insurance managers Salaries by Position

Sagesure insurance managers Opening Jobs

Discussion & Interview Experiences

Discussion & Interview Experiences