Prepare for and practice interview questions from Spokeo.

Spokeo Interview Questions

Spokeo Interview Guides

Data Structures & Algorithms

Compute Deviation

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Find the missing integer from a array of consequtive integers

Find the Missing Number

Find Bigrams

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Booking Regression

Job Recommendation

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Comments Histogram

Write a query that returns all neighborhoods that have 0 users.

Empty Neighborhoods

Calculate the first touch attribution for each `user_id` that converted.

First Touch Attribution

Closest SAT Scores

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

Ad Raters

A/B Testing

Experiment Validity

How do we go about selecting the best 10,000 customers for the pre-launch?

Pre-Launching Shows

Delivery Estimate Model

Button AB Test

A/B Test Power Size

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

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

Your Strengths and Weaknesses

Brainteasers

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

Describe a data project you worked on. What were some of the challenges you faced?

Describing a data project and its challenges

Hurdles In Data Projects

Analytics

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

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

What are the assumptions of linear regression?

Assumptions of Linear Regression

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

Bagging vs Boosting

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

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>

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

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

Xgboost vs Random Forest

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

Random Forest Explanation

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

Let’s say that you work at a B2B SAAS company that’s interested in testing the pricing of different levels of subscriptions.

Your project manager comes to you and asks you to run a two-week-long A/B test to test an increase in pricing.

How would you approach designing this test? How would you determine whether the increase in pricing is a good business decision?

Testing Price Increase

Tell me about a project in which you had to clean and organize a large dataset.

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Data Pipelines

Create a class named <code>LRUCache</code> that implements the behavior expected of a Least Recently Used (LRU) Cache.

The <code>LRUCache</code> class implements the following methods:

<ul>
<li><code>__init__(self, capacity)</code> - initializes the object and takes in a capacity parameter specifying the maximum capacity of the cache.</li>
<li><code>get_if_exists(self, key) -&gt; Any|None</code> - gets a value based on a key. If the key exists, returns the associated value. If the key has expired or did not exist in the first place, returns <code>None</code>.</li>
<li><code>put(self, key, value)</code> - places a value inside the cache. In the case wherein the cache is full, invalidates the least recently used element. When two keys collide, the older value should be invalidated in place of the new one.</li>
</ul>

Example:

<pre tabindex="0" class="chroma"><code>cache = LRUCache(2)
cache.put(&#34;sample&#34;, 55)
cache.get_if_exists(&#34;sample&#34;) # returns 55
cache.get_if_exists(&#34;key&#34;) # returns None
cache.put(&#34;hello&#34;, 10) 
cache.put(&#34;sample&#34;, 9)
cache.put(&#34;world&#34;, 5)
cache.get_if_exists(&#34;hello&#34;) # returns None
</code></pre>

Notes:

<ul>
<li>It is assured that the value in the <code>put</code> method will never receive a <code>None</code> value. Moreover, it is assured that the capacity will always be &gt; 0.</li>

<li>All operations must be O(1).</li>

<li>Aside from Python’s standard dictionary implementation (<code>{}</code>), you may not use any other built-in data structures.</li>
</ul>

LRU Cache 1

A team wants to A/B test multiple different changes through a sign-up funnel.

For example, on a page, a button is currently red and at the top of the page. They want to see if changing a button from red to blue and/or from the top of the page to the bottom of the page will increase click-through.

How would you set up this test?

Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

A/B Test Variants

Let’s say we want to launch a re-design of a landing page to improve the click-through rate. We can do this by implementing an AB test.
Given that we launch an AB test, how would you infer if the results of the click-through rate were statistically significant or not?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

Statistically Significant Test

Precisely ascertain whether the outcomes of an A/B test, executed to assess the impact of a landing page redesign, exhibit statistical significance.

What are the key differences between classification models and regression models?

What is the MOST important difference between regression and classification models?

What is the MOST important difference between regression and classification models?

Classification vs Regression

Classification and Regression

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on \(O(n)\) time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

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?

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Bank Fraud Model

Let’s say you work at Allstate. Allstate is running <code>N</code> online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).

Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types

Note: Please return only one query with each number in a different row

Example:

Input:

<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>join_type</code></td>
<td>VARCHAR</td>
</tr>

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

Size of Joins

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

Regularization and Validation

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Lasso vs Ridge

<h1>Binary Tree Validation</h1>

You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).

A valid BST is defined as follows:

<ul>
<li>The left subtree of a node contains only nodes with values less than or equal to the node’s value.</li>
<li>The right subtree of a node contains only nodes with values greater than or equal to the node’s value.</li>
<li>Both the left and right subtrees must also be binary search trees.</li>
</ul>

Given the function <code>def is_valid_bst(root: Node) -&gt; bool:</code>, return True if the binary tree is a valid BST. Otherwise, return False.

<h3>Example:</h3>

Input:

<img src="/s3/questions/9664817c-3e12-4a40-9236-8382b13f55a0.png" alt="image"/>Converted Binary Tree.png

Output:

<pre tabindex="0" class="chroma"><code>def is_valid_bst(Node(3)) -&gt; True
</code></pre>

Given the root node, verify if a binary search tree is valid or not.

Binary Tree Validation

How would you convey insights and the methods you use to a non-technical audience?

Making data-driven insights actionable for those without technical expertise

Simple Explanations

Challenge

<h3>Job Description</h3><div>Job Description<div>NOTE: You will only be contacted by a specific email or by persons whose email address is from the actual @spokeo.com email domain.About Spokeo:Join our mission to make the world more transparent with data.Spokeo is a people search engine that helps over 18 million monthly visitors reconnect with friends, reunite with families, and protect against fraud. Additionally, our 12 billion records and over 250 million unique profiles help business professionals locate people and assets, research criminal investigation subjects, and more.Founded in 2006, we have grown to a company of nearly 200 dedicated employees, with flexible work locations and an average tenure of 5.3 years. Find out why we were named a “Best Company” by Comparably for Women, Compensation, Happiest Employees, Company Perks & Benefits, and Work-Life Balance, as well as “Best CEO” for co-founder Harrison Tang. About this OpportunityAs a Senior SEO Data Analyst, you will play a critical role in monitoring, analyzing, and improving our SEO performance. You’ll leverage Databricks, Google Analytics, Google Search Console, Tableau, seoClarity, and many other tools to uncover trends, diagnose issues, and develop recommendations that enhance Spokeo’s search presence and increase organic traffic to the website. You will collaborate cross-functionally with product, design, development, and data science teams to refine our SEO and GEO strategy and support broader acquisition growth efforts. This role is ideal for an SEO professional who is data-driven, technically adept, and eager to apply advanced analytical skills to search engine optimization. What You’ll Do:<ul><li>Analyze and optimize SEO performance using a variety of tools.</li><li>Research customer behavior in our data lake using PySpark on Databricks.</li><li>Develop SEO dashboards in Tableau and ensure accurate reporting.</li><li>Partner with cross-functional Scrum teams to test and improve SEO strategies.</li><li>Identify technical SEO opportunities and provide data-backed recommendations.</li><li>Stay ahead of SEO and GEO trends, algorithm updates, and competitor strategies.</li><li>Communicate insights clearly to business and executive stakeholders.</li></ul>Who You Are: <ul><li>5+ years of data analysis experience in a large-scale data-driven web business.</li><li>3+ years of experience with web analytics software such as Google Search Console, Google Analytics, etc. </li><li>3+ years of experience creating dashboards and reports in Tableau.</li><li>Proficiency in PySpark and SQL to extract and analyze data. </li><li>Excellent communication skills—able to turn complex data into clear insights.</li></ul><ul></ul>Highly Desired Skills:<ul><li>Experience with seoClarity or similar SERP research tools such as Ahrefs, SEMrush, or Screaming Frog.</li><li>Experience with Databricks, Jupyter Notebooks, or similar PySpark environments</li><li>Familiarity with keyword research, EEAT strategy, and similar content analysis </li></ul>Nice-to-Have Skills:<ul><li>Knowledge of technical SEO tools such as PageSpeed Insights, Lighthouse, APM, etc.</li></ul><ul></ul><ul></ul>Working at SpokeoSpokeo offers a bonus program, equity plans, and 401K. Once a year, we do a discretionary, merit-based salary increase. Additional benefits include 100% medical/dental/vision coverage and unlimited employee PTO.Spokeo extends written offers to candidates who successfully complete their selection process. Spokeo’s offers include a base salary, participation in a company bonus program, stock options, and comprehensive benefits. A final offer will depend on several factors, including, but not limited to, marketplace competition, job leveling, the candidate’s experience, skills, etc. Privacy Notice for Candidates: https://www.spokeo.com/recruiting-policy Spokeo is an equal opportunity employer. Applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status. Spokeo fosters a business culture where ideas and decisions from all people help us grow, innovate, create the best products, and be relevant in a rapidly changing world.Recruiters or staffing agencies: Spokeo is not obligated to compensate any external recruiter or search firm who presents a candidate or their resume or profile to a Spokeo employee without 1) a current, fully executed agreement on file, and 2) being assigned to the open position (as a search) via our applicant tracking solution.#LI-Remote </div></div>

<h3>Job Description</h3><div>Job Description<div>About Spokeo:Join our mission to make the world more transparent with data.Spokeo is a people search engine that helps over 18 million monthly visitors reconnect with friends, reunite with families, and protect against fraud. Additionally, our 12 billion records and over 250 million unique profiles help business professionals locate people and assets, research criminal investigation subjects, and more.Founded in 2006, we have grown to a company of nearly 200 dedicated employees, with flexible work locations and an average tenure of 5.3 years. Find out why we were named a “Best Company” by Comparably for Women, Compensation, Happiest Employees, Company Perks & Benefits, and Work-Life Balance, as well as “Best CEO” for co-founder Harrison Tang.About this OpportunitySpokeo is seeking a Data Engineering Manager to help build a state-of-the-art analytics platform. As a Data Engineering Manager, you will report directly to our Director of Data Engineering and lead a cross-functional team of data engineers to deliver high-quality, scalable data solutions that meet our data, reporting, and analytics needs. You will provide leadership and technical skills to champion and implement data governance, data integrity, data engineering standards, data integration, and QA best practices. You will partner closely with our Product Owners to understand requirements, provide technical guidance to the team, and ensure the timely delivery of quality solutions.What you’ll do:<ul><li>Lead and manage data engineers to build data solutions for our business intelligence needs </li><li>Lead a cross-functional team in building a state-of-the-art data analytics platform </li><li>Collaborate with data scientists, analysts, product managers, and executives to understand and fulfill our data needs </li><li>Lead database development, data pipeline, integration, ETL, modeling, and data architecture using AWS technologies </li><li>Define and manage SLA’s for all data sets and reporting processes </li><li>Develop software engineering best practices, guidelines, procedures, and repeatable and scalable frameworks </li><li>Document, automate, and enforce data quality, data integrity, and QA best practices</li></ul>What you’ve done:<ul><li>7+ years of experience in Business Intelligence and Data Warehousing </li><li>5+ years of experience with cloud solutions (AWS, Azure) </li><li>5+ years of experience with big data technologies (Spark, Hadoop, MapReduce, Hive, Kafka) </li><li>5+ years of experience with data pipelines orchestration tools like Airflow. </li><li>2+ years of experience managing data engineering teams, providing technical guidance, and mentoring data engineers. </li><li>Expertise in data lakes, warehouses, modeling, ETL, and big data platforms. </li><li>Familiarity with MLOps to help design and automate and streamline the entire lifecycle of ML models. </li><li>Expertise in RDBMS, SQL, query optimization, and security </li><li>A Bachelor’s degree in Computer Science, Information Management, or related field </li><li>Attention to detail, excellent problem-solving ability, and strong communication skills</li></ul> Working at SpokeoSpokeo offers a bonus program, equity plans, and 401K. Once a year, we do a discretionary, merit-based salary increase. Additional benefits include 100% medical/dental/vision coverage and unlimited employee PTO.Spokeo extends written offers to candidates who successfully complete their selection process. Spokeo’s offers include a base salary, participation in a company bonus program, stock options, and comprehensive benefits. A final offer will depend on several factors, including, but not limited to, marketplace competition, job leveling, the candidate’s experience, skills, etc.Privacy Notice for Candidates: https://www.spokeo.com/recruiting-policySpokeo is an equal opportunity employer. Applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status. Spokeo fosters a business culture where ideas and decisions from all people help us grow, innovate, create the best products, and be relevant in a rapidly changing world. Recruiters or staffing agencies: Spokeo is not obligated to compensate any external recruiter or search firm that presents a candidate or their resume or profile to a Spokeo employee without 1) a current, fully executed agreement on file, and 2) being assigned to the open position (as a search) via our applicant tracking solution.#LI-Remote </div></div>

<h5>Overview </h5>Senior SEO Data Analyst role at Spokeo Spokeo is a people search engine that helps over 18 million monthly visitors reconnect with friends, reunite with families, and protect against fraud. Our 12 billion records and over 250 million unique profiles help business professionals locate people and assets, research criminal investigation subjects, and more. Founded in 2006, we have grown to a company of nearly 200 dedicated employees, with flexible work locations and an average tenure of 5.3 years. Spokeo is an equal opportunity employer and a recipient of recognitions for work-life balance and company culture. <h5>What Youll Do </h5> <ul><li>Analyze and optimize SEO performance using a variety of tools. </li><li>Research customer behavior in our data lake using PySpark on Databricks. </li><li>Develop SEO dashboards in Tableau and ensure accurate reporting. </li><li>Partner with cross-functional Scrum teams to test and improve SEO strategies. </li><li>Identify technical SEO opportunities and provide data-backed recommendations. </li><li>Stay ahead of SEO and GEO trends, algorithm updates, and competitor strategies. </li><li>Communicate insights clearly to business and executive stakeholders. </li> </ul><h5>Who You Are </h5><ul><li>5+ years of data analysis experience in a large-scale data-driven web business. </li><li>3+ years of experience with web analytics software such as Google Search Console, Google Analytics, etc. </li><li>3+ years of experience creating dashboards and reports in Tableau. </li><li>Proficiency in PySpark and SQL to extract and analyze data. </li><li>Excellent communication skillsable to turn complex data into clear insights. </li> </ul><h5>Highly Desired Skills </h5><ul><li>Experience with seoClarity or similar SERP research tools such as Ahrefs, SEMrush, or Screaming Frog. </li><li>Experience with Databricks, Jupyter Notebooks, or similar PySpark environments </li><li>Familiarity with keyword research, EEAT strategy, and similar content analysis </li> </ul><h5>Nice-to-Have Skills </h5><ul><li>Knowledge of technical SEO tools such as PageSpeed Insights, Lighthouse, APM, etc. </li> </ul><h5>Working at Spokeo </h5>Spokeo offers a bonus program, equity plans, and 401K. Once a year, we do a discretionary, merit-based salary increase. Additional benefits include 100% medical/dental/vision coverage and unlimited employee PTO. Spokeo extends written offers to candidates who successfully complete their selection process. Spokeos offers include a base salary, participation in a company bonus program, stock options, and comprehensive benefits. A final offer will depend on several factors, including, but not limited to, marketplace competition, job leveling, the candidates experience, skills, etc. <h5>Legal & EEO </h5>Privacy Notice for Candidates: https://www.spokeo.com/recruiting-policy Spokeo is an equal opportunity employer. Applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status. Spokeo fosters a business culture where ideas and decisions from all people help us grow, innovate, create the best products, and be relevant in a rapidly changing world. <h5>Job Details </h5><ul><li>Seniority level: Not Applicable </li><li>Employment type: Other </li><li>Job function: Marketing and Sales </li><li>Industries: Technology, Information and Internet </li> </ul> #J-18808-Ljbffr

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