Prepare for and practice interview questions from Experis.

Experis Interview Questions

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experis is the global leader in professional resourcing and project-based workforce solutions. we accelerate organizations'​ growth by intensely attracting, assessing, and placing specialized expertise in it, finance, and engineering to precisely deliver in-demand talent for mission-critical positions, enhancing the competitiveness of the organizations and people we serve. | visit www.experis.com to learn more.

Overview

Experis Interview Guides

Machine Learning

How would you encode a categorical variable with thousands of distinct values

Encoding Categorical Features

How do we deal with the missing square footage data to construct our model

Missing Housing Data

How would we build a model to detect fraud and text customers to approve or deny fraudulent transactions

Bank Fraud Model

How would we know if we have enough data to create an accurate enough model?

One Million Rides

Why can the same machine learning algorithm produce different success rates on the same dataset

Same Algorithm Different Success

Data Structures & Algorithms

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

Write a function n_frequent_words that returns the top N frequent words and their frequencies, and state its run-time

Get Top N Frequent Words

Write a function to return the names and resume IDs for resumes whose IDs have not already been scraped

New Resumes

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

Prime to N

Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

Employee Salaries

Select the 2nd highest salary in the engineering department

2nd Highest Salary

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

First Touch Attribution

Write a query to return the two students with the closest test scores and the score difference

Closest SAT Scores

Get the top 3 highest employee salaries by department

Top Three Salaries

Probability

First to Six

What is the probability that each subsequent card is larger than the previous drawn card when picking three cards from a shuffled deck of 500 cards

500 Cards

What is the probability that it's actually raining in Seattle?

Raining in Seattle

What is the probability that item X would be found on Amazon's website?

Found Item

Given three uniform(0,4) random variables, what is the probability that the median of them is greater than 3?

Median Probability

Statistics

Explain what a p-value is to someone who is not technical

P-value to a Layman

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

Perfectly Separable

What are the assumptions of linear regression?

Assumptions of Linear Regression

What is the downside of only using the R-Squared (R^2) value to determine a relationship between two variables

Using R Squared

What statistical test could you use to determine which of two parcel types is better to use, given how often they are damaged?

Different Parcel Effectiveness

Challenge

<p>Write a SQL query to select the 2nd highest salary in the engineering department.</p>

<p><strong>Note:</strong> If more than one person shares the highest salary, the query should select the next highest salary.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<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>
<p><code>departments</code> table</p>

<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>
<p><strong>Output:</strong></p>

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

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


<p>Given a <code>employees</code> and <code>departments</code> table, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<table>
<thead>
<tr>
<th>Columns</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>
<p><code>departments</code> table</p>

<table>
<thead>
<tr>
<th>Columns</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>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>percentage_over_100k</code></td>
<td>FLOAT</td>
</tr>

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

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


<p>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). </p>

<p><em>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.</em> </p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<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>
<p><code>departments</code> table</p>

<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>
<p><strong>Output</strong>:</p>

<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>


<p>Given a table of students and their SAT test scores, write a query to return the two students with the closest test scores with the score difference.</p>

<p>If there are multiple students with the same minimum score difference, select the student name combination that is higher in the alphabet. </p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>scores</code> table</p>

<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>student</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>score</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>one_student</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>other_student</code></td>
<td>VARCHAR</td>
</tr>

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


<p>Given two sorted lists, write a function to merge them into one sorted list.</p>

<p><em>Bonus: What’s the time complexity?</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">list1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">list2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">merge_list</span><span class="p">(</span><span class="n">list1</span><span class="p">,</span><span class="n">list2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
</span></span></code></pre>


<p>You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort?</p>


You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort? 

Merging Lists Complexity

<p>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.</p>

<p>How would you assess the validity of the result?</p>


How would you assess the validity of the result?

Experiment Validity

A/B Testing

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

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

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">N</span> <span class="o">=</span> <span class="mi">3</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">prime_numbers</span><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
</span></span></code></pre>


<p>The schema below is for a retail online shopping company consisting of two tables, <code>attribution</code> and <code>user_sessions</code>.</p>

<ul>
<li><p>The attribution table logs a session visit for each row.</p></li>

<li><p>If <code>conversion</code> is <code>true</code>, then the user converted to buying on that session.</p></li>

<li><p>The <code>channel</code> column represents which advertising platform the user was attributed to for that specific session.</p></li>

<li><p>Lastly the <code>user_sessions</code> table maps many to one session visits back to one user.</p></li>
</ul>

<p>First touch attribution is defined as the channel with which the converted user was associated when they first discovered the website.</p>

<p>Calculate the first touch attribution for each <code>user_id</code> that converted. </p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>attribution</code> table</p>

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

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

<tr>
<td><code>channel</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>conversion</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<p><code>user_sessions</code> table</p>

<table>
<thead>
<tr>
<th>column</th>
<th>type</th>
</tr>
</thead>

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

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Example output:</strong></p>

<table>
<thead>
<tr>
<th><code>user_id</code></th>
<th><code>channel</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>123</td>
<td>facebook</td>
</tr>

<tr>
<td>145</td>
<td>google</td>
</tr>

<tr>
<td>153</td>
<td>facebook</td>
</tr>

<tr>
<td>172</td>
<td>organic</td>
</tr>

<tr>
<td>173</td>
<td>email</td>
</tr>
</tbody>
</table>


<p>Given a table called <code>employees</code>, get the largest salary of any employee by department</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<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>department</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>department</code></td>
<td>VARCHAR</td>
</tr>

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


Write a query to get the largest salary of any employee by department

Largest Salary by Department

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

<p>Give an example of the tradeoffs between the two.</p>


<p>In machine learning, when would you use a bagging algorithm over a boosting algorithm?</p>


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

Bagging vs. Boosting

In which case would you use a bagging algorithm versus a boosting algorithm

Bagging vs Boosting

<p>Amy and Brad take turns in rolling a fair six-sided die. Whoever rolls a “6” first wins the game. Amy starts by rolling first.</p>

<p>What’s the probability that Amy wins?</p>


Amy and Brad take turns in rolling a fair six-sided die. Whoever rolls a “6” first wins the game. Amy starts by rolling first.

What’s the probability that Amy wins?

<p>Write a query to identify the manager with the biggest team size.</p>

<p>You may assume there is only one manager with the largest team size.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<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>

<tr>
<td><code>manager_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>managers</code> table</p>

<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>

<tr>
<td><code>team</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>manager</code></td>
<td>VARCHAR</td>
</tr>

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


Manager Team Sizes

<p>You are about to get on a plane to Seattle. You want to know if you should bring an umbrella. You call 3 random friends of yours who live there and ask each independently if it’s raining. Each of your friends has a <sup>2</sup>⁄<sub>3</sub> chance of telling you the truth and a <sup>1</sup>⁄<sub>3</sub> chance of messing with you by lying. All 3 friends tell you that “Yes” it is raining.</p>

<p>What is the probability that it’s actually raining in Seattle?</p>


<p>What is the <strong>main</strong> difference between the Frequentist and Bayesian methods of probabilistic analysis?</p>


What is the **main** difference between the Frequentist and Bayesian methods of probabilistic analysis?

Frequentist Vs Bayesian Analysis

<p>Let’s say you run a wine house. You have detailed information about the chemical composition of wines in a <code>wines</code> table.</p>

<p>One day, a customer comes asking specifically for a wine that has</p>

<ul>
<li>Greater or equal to than 13% alcohol content</li>
<li>Ash content less than 2.4</li>
<li>A color intensity less than 3</li>
</ul>

<p>Write a query to find the <code>id</code>s of suitable wines for this customer.</p>

<p><em>Note:</em> All percentages are reported with two numbers before the decimal point; for example, 13.55% is represented as <code>13.55</code> instead of <code>0.1355</code>.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>wines</code> table</p>

<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>alcohol</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>malic_acid</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>ash</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>alcalinity_of_ash</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>magnesium</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>total_phenols</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>flavanoids</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>nonflavanoid_phenols</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>proanthocyanins</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>color_intensity</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>hue</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>od280_or_od315_of_diluted_wines</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>proline</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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


Let’s say you run a wine house. You have detailed information about the chemical composition of wines in a wines table.

SELECTive Wine Connoisseur

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


<p>What does a p-value in a statistical test represent?</p>

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

P-value to a Layman I

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

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>Imagine a deck of 500 cards numbered from 1 to 500. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previous drawn card?</p>


<p>Imagine a deck of 2000 cards numbered from 1 to 2000. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previously drawn card?</p>


Imagine a deck of 2000 cards numbered from 1 to 2000. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what's the probability of each subsequent card being larger than the previously drawn card?

2000 cards

<p>Over budget on a project is defined when the salaries, prorated to the day, exceed the budget of the project.</p>

<p>For example, if Alice and Bob both combined income make 200K and work on a project of a budget of 50K that takes half a year, then the project is over budget given 0.5 * 200K = 100K &gt; 50K.</p>

<p>Write a query to forecast the budget for all projects and return a label of <code>&#34;overbudget&#34;</code> if it is over budget and <code>&#34;within budget&#34;</code> otherwise.</p>

<p><em>Note: Assume that employees only work on one project at a time.</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<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>
<p><code>projects</code> table</p>

<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>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>state_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>budget</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>departments</strong> table</p>

<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>
<p><code>employee_projects</code> table</p>

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

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

<tr>
<td><code>employee_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>project_forecast</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>


Write a query to forecast each project's budget and label it overbudget or within budget

Over-Budget Projects

<p>Tell me about a data project that didn’t go the way you expected. What did you set out to do, what surprised you, and how did you handle it?</p>


Describing a data project and its challenges

Hurdles In Data Projects

Behavioral

<p>Let’s say you’re working in HR at a major tech company, and they ask you to find employees with a high probability of leaving the company. They tell you these employees perform well but have the lowest pay.</p>

<p>Given two tables, <code>employees</code>  and <code>projects</code>, find the five lowest-paid employees who have completed at least three projects.</p>

<p>Note: We consider projects to be completed when they have an end date, which is the same as saying their <code>End_dt</code> is not <code>NULL</code>.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<table>
<thead>
<tr>
<th>id</th>
<th>salary</th>
</tr>
</thead>

<tbody>
<tr>
<td>INTEGER</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><code>projects</code> table</p>

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>Start_dt</th>
<th>End_dt</th>
</tr>
</thead>

<tbody>
<tr>
<td>INTEGER</td>
<td>INTEGER</td>
<td>DATETIME</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>employee_id</th>
</tr>
</thead>

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


Find the five employees with the hightest probability of leaving the company

Top 5 Turnover Risk

<p>You have an array of integers, <code>nums</code> of length <code>n</code> spanning <code>0</code> to <code>n</code> with one missing. Write a function <code>missing_number</code> that returns the missing number in the array.</p>

<p><em>Note: Complexity of \(O(n)\) required.</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">nums</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span> 
</span></span><span class="line"><span class="cl"><span class="n">missing_number</span><span class="p">(</span><span class="n">nums</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mi">3</span>
</span></span></code></pre>


<p>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.).</p>

<p>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</p>

<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>

<p><em>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</em></p>

<p><em>Note: Please return only one query with each number in a different row</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<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>
<p><strong>Output:</strong></p>

<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>


Create top_ads with the top 3 ads and return the row counts for inner, left, right, and cross joins with ads

Size of Joins

<p>There are two tables. One table is called <code>swipes</code> that holds a row for every Tinder swipe and contains a boolean column that determines if the swipe was a right or left swipe called <code>is_right_swipe</code>. The second is a table named <code>variants</code> that determines which user has which variant of an AB test.</p>

<p>Write a SQL query to output the average number of right swipes for two different variants of a feed ranking algorithm by comparing users that have swiped 10, 50, and 100 swipes in a <code>feed_change</code> experiment.</p>

<p><em>Note: Users have to have swiped at least 10 times to be included in the subset of users to analyze the mean number of right swipes.</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>variants</code> table</p>

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

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

<tr>
<td><code>experiment</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>variant</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>swipes</code> table</p>

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

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

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>swiped_user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>is_right_swipe</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>variant</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>mean_right_swipes</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>swipe_threshold</code></td>
<td>INTEGER</td>
</tr>

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


Write a SQL query to find the average number of right swipes for different ranking algorithms.

Swipe Precision

<p>Let’s say you have a categorical variable with thousands of distinct values, how would you encode it?</p>


<p>Which method can be used to extract communities from large networks, that does not require a pre-determined number of clusters like K-means?</p>

Which method can be used to extract communities from large networks, that does not require a pre-determined number of clusters like K-means?

<p>We’re given two tables. One is named <code>projects</code> and the other maps employees to the projects they’re working on. </p>

<p>We want to select the five most expensive projects by budget to employee count ratio. But let’s say that we’ve found a bug where there exist duplicate rows in the <code>employee_projects</code> table.</p>

<p>Write a query to account for the error and select the top five most expensive projects by budget to employee count ratio.</p>

<h3>Schema</h3>

<p><strong>Input:</strong></p>

<p><code>projects</code> table</p>

<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>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>state_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>budget</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>employee_projects</code> table</p>

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

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

<tr>
<td><code>employee_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>budget_per_employee</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>

<h3>Example</h3>

<p><strong>Input:</strong></p>

<p><strong><code>projects</code> table</strong></p>

<table>
<thead>
<tr>
<th>id</th>
<th>title</th>
<th>start_date</th>
<th>end_date</th>
<th>budget</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>party</td>
<td>2006-04-17 00:00:00</td>
<td>2006-05-19 00:00:00</td>
<td>25875</td>
</tr>

<tr>
<td>2</td>
<td>diversity</td>
<td>2017-08-09 00:00:00</td>
<td>2017-09-18 00:00:00</td>
<td>77867</td>
</tr>

<tr>
<td>3</td>
<td>integration</td>
<td>2005-06-29 00:00:00</td>
<td>2005-07-19 00:00:00</td>
<td>75987</td>
</tr>

<tr>
<td>4</td>
<td>testing</td>
<td>2009-01-12 00:00:00</td>
<td>2009-05-03 00:00:00</td>
<td>35946</td>
</tr>

<tr>
<td>5</td>
<td>launch</td>
<td>2005-05-02 00:00:00</td>
<td>2005-10-16 00:00:00</td>
<td>66292</td>
</tr>

<tr>
<td>6</td>
<td>meet</td>
<td>2001-09-09 00:00:00</td>
<td>2002-04-04 00:00:00</td>
<td>71243</td>
</tr>

<tr>
<td>7</td>
<td>payroll</td>
<td>2019-09-25 00:00:00</td>
<td>2020-05-05 00:00:00</td>
<td>97071</td>
</tr>

<tr>
<td>8</td>
<td>admin</td>
<td>2000-07-08 00:00:00</td>
<td>2000-09-10 00:00:00</td>
<td>24000</td>
</tr>

<tr>
<td>9</td>
<td>petronas</td>
<td>2016-07-01 00:00:00</td>
<td>2016-11-28 00:00:00</td>
<td>37088</td>
</tr>

<tr>
<td>10</td>
<td>bel</td>
<td>2005-08-03 00:00:00</td>
<td>2006-04-29 00:00:00</td>
<td>61937</td>
</tr>
</tbody>
</table>
<p><strong><code>employee_projects</code> table</strong></p>

<table>
<thead>
<tr>
<th>project_id</th>
<th>employee_id</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>7</td>
</tr>

<tr>
<td>1</td>
<td>9</td>
</tr>

<tr>
<td>2</td>
<td>6</td>
</tr>

<tr>
<td>3</td>
<td>1</td>
</tr>

<tr>
<td>3</td>
<td>2</td>
</tr>

<tr>
<td>4</td>
<td>5</td>
</tr>

<tr>
<td>5</td>
<td>3</td>
</tr>

<tr>
<td>5</td>
<td>4</td>
</tr>

<tr>
<td>6</td>
<td>8</td>
</tr>

<tr>
<td>8</td>
<td>10</td>
</tr>

<tr>
<td>9</td>
<td>11</td>
</tr>

<tr>
<td>10</td>
<td>12</td>
</tr>

<tr>
<td>5</td>
<td>5</td>
</tr>

<tr>
<td>9</td>
<td>11</td>
</tr>

<tr>
<td>6</td>
<td>8</td>
</tr>

<tr>
<td>4</td>
<td>5</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>title</th>
<th>budget_per_employee</th>
</tr>
</thead>

<tbody>
<tr>
<td>diversity</td>
<td>77867</td>
</tr>

<tr>
<td>meet</td>
<td>71243</td>
</tr>

<tr>
<td>bel</td>
<td>61937</td>
</tr>

<tr>
<td>integration</td>
<td>37993.5</td>
</tr>

<tr>
<td>petronas</td>
<td>37088</td>
</tr>
</tbody>
</table>


Project Budget Error

<p>Say you are tasked with designing a data mart or data warehouse for a new online retailer.</p>

<p>How would you design the system?</p>

<p><em>Note: Sketch a star schema to explain your design.</em></p>


Design a data warehouse for a new online retailer

Retailer Data Warehouse

Data Modeling

<p>Let’s say we have a very naive advertising platform. There is an audience of size <em>A</em> and a limited amount of impressions <em>B</em>.</p>

<p>Each of the impressions goes to just one user, defined at random. Every user in the audience has the same random chance of receiving each impression. </p>

<ol>
<li><p>Compute the probability that a user has exactly 0 impressions. </p></li>

<li><p>What’s the probability that every person has at least 1 impression?</p></li>
</ol>


Compute the probability a user gets exactly 0 impressions, and the probability everyone gets at least 1 impression

Impression Reach

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

<p>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.</p>

<p>How would we build this model?</p>


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

<p>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.</p>

<p>Which statement is true?</p>


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?

<p>You’re given a string that may contain the characters <strong><code>{</code></strong>, <strong><code>}</code></strong>, <strong><code>[</code></strong>, <strong><code>]</code></strong>, <strong><code>(</code></strong>, and <strong><code>)</code></strong>.</p>

<p><strong>Task:</strong> Verify that the string is balanced. A balanced string is one where every opening character, <strong><code>{</code></strong>, <strong><code>[</code></strong>, or <strong><code>(</code></strong>, has a corresponding closing character, <strong><code>}</code></strong>, <strong><code>]</code></strong>, or <strong><code>)</code></strong>.</p>

<p>Write a function called <strong><code>is_balanced(string: str) -&gt; bool</code></strong> which verifies the balance of a string.</p>

<p><strong>Example:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;(())[]</span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">True</span>
</span></span></code></pre>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;{([()</span><span class="si">{}</span><span class="s1">])()}&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">True</span>
</span></span></code></pre>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">[]())&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">False</span>
</span></span></code></pre>

<hr/>


Write a function that tests whether a string of brackets is balanced.

The Brackets Problem

<p>Netflix has hired people to rate movies.</p>

<p>Out of all of the raters, 80% of the raters carefully rate movies and rate 60% of the movies as good and 40% as bad. The other 20% are lazy raters and rate 100% of the movies as good.</p>

<p>Assuming all raters rate the same amount of movies, what is the probability that a movie is rated good?</p>


<p>There are two categories of movie raters: careful and lazy. Careful raters make up 80% of the total. They rate 60% of the movies as good and 40% as bad. Lazy raters, constitute 20% of the raters and 100% of the movies as good. Given these parameters, calculate the overall probability of a movie being rated good.</p>

There are two categories of movie raters: careful and lazy. Careful raters make up 80% of the total. They rate 60% of the movies as good and 40% as bad. Lazy raters, constitute 20% of the raters and 100% of the movies as good. Given these parameters, calculate the overall probability of a movie being rated good.

Lazy Raters

What is the probability that a movie is rated good

<p>We’re given two tables. One is named <code>projects</code> and the other maps employees to the projects they’re working on.</p>

<p>Write a query to get the top five most expensive projects by budget to employee count ratio.</p>

<p><em>Note: Exclude projects with 0 employees. Assume each employee works on only one project.</em></p>

<p><code>projects</code> table</p>

<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>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>state_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>budget</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>employee_projects</code> table</p>

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

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

<tr>
<td><code>employee_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>title</code></td>
<td>VARCHAR</td>
</tr>

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


Write a query to get the top five projects by budget-to-employee-count ratio

Employee Project Budgets

<p><strong>Lead Data & AI Scientist </strong></p><p><strong>Hybrid: 1–2 days per week in the office (London or North West)</strong></p><p><strong>Permanent</strong></p><p><strong>£100k-£130k DOE + Bonus + Benefits</strong></p><p><br></p><p>Experis are delighted to be partnering with a leading and well-respected organisation as they continue to invest heavily in Data Science, Artificial Intelligence and Generative AI. We are supporting them in the search for an experienced Lead Data Scientist to shape, scale and lead their enterprise-wide AI capability.</p><p><br></p><p>This is a high-impact, strategic role with responsibility for defining the direction of Data Science and AI across the organisation. You will lead the delivery of production-grade ML and GenAI solutions, build a high-performing specialist team, and partner closely with senior stakeholders to drive measurable business value.</p><p><br></p><p><strong>What You’ll Be Doing</strong></p><ul><li>Leading the design, development and productionisation of Machine Learning, AI and GenAI solutions across the business.</li><li>Defining technical standards, best practices and governance for Data Science and MLOps.</li><li>Identifying and prioritising high-impact use cases in partnership with Wealth, Enablement and Technology teams.</li><li>Building, mentoring and developing a high-performing Data Science team.</li><li>Embedding modern MLOps practices including CI/CD, model monitoring, feature stores and version control.</li><li>Working closely with Data Engineering, Architecture and Operations teams to ensure scalable, secure deployment.</li><li>Acting as a senior technical authority and thought leader in advanced analytics and AI.</li><li>Championing innovation, experimentation and continuous improvement in AI and analytics delivery.</li></ul><p><br></p><p><strong>Experience Required</strong></p><ul><li>Proven experience in Head of or lead-level Data Science, AI or Machine Learning roles within enterprise environments.</li><li>Strong Python and SQL skills, with experience developing production-grade analytical solutions.</li><li>Hands-on experience with cloud platforms (Azure, AWS and/or Snowflake).</li><li>Expertise in modern machine learning frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow).</li><li>Demonstrable experience delivering end-to-end ML and GenAI solutions (including RAG, LLMs, embeddings and vector databases).</li><li>Strong knowledge of MLOps tooling and practices (MLflow, CI/CD, containerisation, monitoring).</li><li>Experience leading, mentoring and developing technical teams.</li></ul><p><br></p><p>If you’d like to learn more, please contact <strong>Jacob Ferdinand</strong> at <strong>Jacob.Ferdinand@experis.co.uk</strong></p>

<h3>Job Description</h3><div>Job Description<div>About the Role <br /><p>Enjoy turning messy data into clean, client-ready insights? Want to build systems that make work faster and smarter?<br /></p><div><br /></div><p>We’re hiring a <b>Data & RFP Analyst</b> to support our Client Service team at ExperINS. This role sits at the center of our busiest work, managing RFPs, building high-quality Excel deliverables, and improving how we handle data across the business.<br /></p><div><br /></div><div>The Data Analyst supports business growth and client retention by transforming data into actionable insights, improving internal processes, and delivering analytics that strengthen sales opportunities, renewal strategy, and ongoing client consulting.<br /></div><p><br /></p><p>You’ll own critical workflows during peak seasons, while also helping us automate, streamline, and scale how we operate.<br /></p><p><br /></p><p>This is not just execution. This is building better systems.<br /></p>What You’ll Do<br />Core Execution<br /><ul><li>Partner with Sales team through diligence review. Prepare prospect and finalist claims and benchmarking reporting. Create executive-ready charts, exhibits and presentation materials for prospect and finalist presentations. <br /></li><li>Manage multiple RFPs at once and proposal processes through data gathering, formatting, quality review, and deadline coordination<br /></li><li>Develop renewal and prospect analytics including contribution modeling, plan comparisons, rate analysis, and cost projections<br /></li><li>Support service team by gathering data including plan performance materials, benchmarking, contribution strategies, and renewal projections<br /></li><li>Prepare client-ready reporting and presentation materials for pre-renewal, renewal, and strategic planning meetings<br /></li></ul>Automation & Process Improvement<br /><ul><li>Build and maintain Excel macros, templates, and automated workflows<br /></li><li>Identify inefficiencies and redesign processes to reduce manual work<br /></li><li>Standardize templates, data inputs, and file structures across the team<br /></li><li>Improve turnaround time, consistency, and scalability of RFP execution<br /></li><li>Help create repeatable systems that make high-volume work easier to manage<br /></li></ul>Data Systems, SQL & Python<br /><ul><li>Support the building and ongoing management of internal SQL databases and data structures<br /></li><li>Write and use basic SQL queries to pull, validate, and organize data<br /></li><li>Use Python to automate repetitive tasks, improve data workflows, and support internal tools<br /></li><li>Partner with technical resources to expand automation and strengthen data infrastructure<br /></li><li>Help improve how ExperINS captures, stores, and uses data across the business<br /></li></ul><br />Requirements<div>What We’re Looking For<br />Must-Have<br /><ul><li>Advanced Excel skills, including XLOOKUP, INDEX/MATCH, pivot tables, and working with large datasets<br /></li><li>Experience building or using Excel macros and VBA<br /></li><li>Strong attention to detail and data accuracy<br /></li><li>Ability to manage multiple projects and deadlines at once<br /></li><li>Comfort working with both structured and messy data<br /></li><li>Interest in automation and process improvement<br /></li><li>Working knowledge of basic SQL queries<br /></li><li>Experience with Python for data cleanup, workflow support, or automation<br /></li></ul>Nice-to-Have<br /><ul><li>Experience with employee benefits and/or insurance <br /></li><li>Experience building or managing SQL databases<br /></li><li>Experience with process automation or workflow design<br /></li><li>Experience with reporting or visualization tools such as Power BI, Tableau, or Zoho Analytics<br /></li><li>Familiarity with RFPs or other client-facing deliverables<br /></li><li>Experience working with healthcare or employee benefits data<br /></li></ul></div><div><br /></div><br />BenefitsWhy Join ExperINS<br /><p>We invest in our people so they can do their best work and build a career here.<br /></p><p><b>Benefits</b><br /></p><ul><li><b>Health Coverage:</b> Medical, dental, and vision plans<br /></li><li><b>Retirement:</b> 3% 401(k) contribution, no employee match required<br /></li><li><b>Time Off:</b> Unlimited PTO plus 14 company holidays<br /></li><li><b>Wellness:</b> Onsite gym access, plus local and travel gym discounts<br /></li><li><b>Hybrid Work:</b> 3–4 days in-office, built around collaboration and team connection<br /></li></ul>How We Operate<br /><p>We are a performance-driven team with a strong culture. Our values are not posters, they show up in how we work every day.<br /></p><ul><li><div><b>Get Sh*t Done, Have Fun</b><br /></div><div>We move fast, take ownership, and enjoy the work.<br /></div></li><li><div><b>Elevate Awesomeness</b><br /></div><div>We push for better, in ourselves and in our teammates.<br /></div></li><li><div><b>No A**</b><b>holes</b><b><br /></b></div><div>We hire and work with people who respect others.<br /></div></li><li><div><b>Leave a Ladder</b><br /></div><div>We help others grow and make the path easier for the next person.<br /></div></li><li><div><b>Advocate Relentlessly</b><br /></div><div>We do what is right for our clients and our team.<br /></div></li></ul><div><br /></div><br /></div></div>

<p>Requirements Gathering & Analysis</p><p><strong>Business Analysis & Stakeholder Management</strong></p><ul><li>Engage with business stakeholders to elicit, analyse, and document business and technical requirements</li><li>Facilitate stakeholder workshops, discussions, and requirements walkthroughs</li><li>Translate business requirements into clear user stories, acceptance criteria, and functional specifications</li><li>Proactively manage stakeholder expectations, clearly communicating progress, risks, and dependencies</li></ul><p>Technical Analysis & Delivery</p><ul><li>Support API integrations, including defining, documenting, and validating API contracts</li><li>Write, optimise, and review complex SQL queries for data extraction, validation, and analysis</li><li>Assist with API testing and validation using tools such as Postman</li><li>Produce high‑quality documentation including BRDs, FSDs, process flows, and use‑case definitions</li></ul><p>Agile Delivery & Tools</p><ul><li>Own and manage requirements within Jira, ensuring full end‑to‑end traceability</li><li>Participate in backlog refinement, sprint planning, and defect triage activities</li><li>Collaborate closely with cross‑functional teams including Development, QA, and Product</li><li>Maintain well‑structured documentation within Confluence</li></ul><p>Required Skills & Experience</p><ul><li>Proven experience working as a <strong>Technical Business Analyst</strong> (3–8 years)</li><li>Strong stakeholder management and communication skills across technical and non‑technical audiences</li><li>Hands‑on experience with <strong>API integrations</strong> and API contract definition</li><li>Advanced <strong>SQL</strong> skills with experience working on complex datasets</li><li>Strong experience using <strong>Jira</strong> for user stories, backlog management, and requirement traceability</li><li><strong>Mandatory insurance domain experience</strong>, covering the full policy lifecycle including underwriting, claims, renewals, and policy administration</li></ul><p></p>

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

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