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Data Modeling

Swiping App Design

Design a database for a ride-sharing app.

Ride-Sharing App Schema

Machine Learning

Random Forest Explanation

Xgboost vs Random Forest

Data Structures & Algorithms

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

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

Binary Tree Validation

Brainteasers

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

Your Strengths and Weaknesses

Analytics

Describing a data project and its challenges

Hurdles In Data Projects

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

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

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

<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="https://d2qpirhrfplx04.cloudfront.net/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>

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

Design a database to represent a Tinder style dating app. 

What does the schema look like and what are some optimizations that you think we might need?

Let’s say you are making a swiping app, similar to Tinder.

What would be the most efficient way (in terms of retrieval time) to store which users have swiped on each other on a database backend?

Let's say you are making a swiping app, similar to Tinder.

What would be the most efficient way (in terms of retrieval time) to store which users have swiped on each other on a database backend?

Swipes Database Design

Let’s say you work at a company developing a new ride-sharing app.

How would you design a database that could record rides between riders and drivers?

What would the schema of the tables look like and how would they join together?

Challenge

Andela Salaries by Position

<h5>Staff Machine Learning Engineer </h5>At Andela, we know brilliance is evenly distributed around the world, but opportunity is not. For over 10 years, Andela has connected its customers with top global, remote technical talent from over 135 countries with the majority residing in emerging markets like Africa and Latin America. As one of the world's largest talent marketplaces, Andela gives companies greater flexibility to quickly deploy qualified technologists. With talent highly skilled in advanced technologies to support Application Development, Artificial Intelligence, Cloud & DevOps, Data Engineering, and much more, customers experience 33% faster project delivery. The company's exclusive AI-powered platform, Andela Talent Cloud, is the industry's only unified platform managing the complete global talent lifecycle and enables customers to fill individual roles or engage fully managed teams up to 66% faster. Andela is on the precipice of two breakout industry transformations: one in staffing/hiring and the other in software development, both accelerated by generative AI. Join us and change the world. We are seeking a Staff Machine Learning Engineer to join our dynamic team specializing in a technology marketplace platform for engineering talent and delivering cutting-edge Generative AI (GenAI) managed solutions for enterprise clients. In this strategic role, you will be responsible for designing, developing, and deploying scalable machine learning algorithms and systems. Your proven experimental mindset and extensive practical experience will be key in optimizing our platform's performance, improving our GenAI offerings, and driving measurable impact across diverse business use cases. <h5>Exceptional Leadership: </h5>As an Andelan, you don't need to be a people manager to be a leader, and you'll serve as a role model for the rest of the company. Think about the feedback your peers typically give you if it usually sounds like the below, we want to hear from you. <ul> <li>Low ego, low drama, servant leader: You share credit, take blame. You like being wrong because it means someone else had an even better idea. </li><li>One team mentality: You break silos across teams. You put the company and mission first above yourself and team alone. </li><li>Great listener, hungry for feedback: You're always seeking to improve our product, our business, yourself. You solicit diverse opinions and deeply listen. </li><li>Owner, not renter: You see a problem, you fix it or find someone who will. The buck stops with you. </li><li>Player-coach: You roll up your sleeves and get scrappy. You do this by proactively collaborating with your team while actively engaging in important details. </li><li>Business problem solver: You're not just a functional expert; you consistently get praise for approaching your function through the lens of solving business problems. </li></ul> <h5>Key Responsibilities: </h5><ul> <li>Design, build, and maintain robust and scalable machine learning models and pipelines tailored to enhance our marketplace platform and GenAI managed solutions. </li><li>Lead experimentation processes, rigorously validating models through A/B testing, statistical analyses, and iterative improvements. </li><li>Collaborate closely with data scientists, software engineers, product managers, and client teams to define requirements, build solutions, and deliver impactful, high-quality products. </li><li>Continuously improve existing ML solutions, ensuring they are performant, efficient, and aligned with evolving business objectives. </li><li>Ensure adherence to best practices in code quality, data integrity, and infrastructure reliability, with a strong emphasis on scalability and maintainability. </li><li>Mentor junior engineers and contribute actively to team growth, knowledge sharing, and fostering an experimental, innovation-driven engineering culture. </li><li>Stay current with advancements in machine learning, AI methodologies, and marketplace technologies to propose innovative solutions proactively. </li></ul> <h5>Qualifications: </h5><ul> <li>Bachelor's degree or higher in Computer Science, Engineering, Data Science, or a related technical field. </li><li>Proven experience implementing machine learning algorithms and solutions at scale in production environments. </li><li>Demonstrated experimental mindset, adept at hypothesis testing, experimentation design, and rigorous validation techniques. </li><li>Expert proficiency in Python and experience with frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and related ML technologies. </li><li>Extensive experience building and deploying scalable ML pipelines and working within cloud infrastructure environments such as AWS, GCP, or Azure. </li><li>Strong understanding of data structures, algorithm design, and performance optimization techniques. </li><li>Exceptional problem-solving abilities, coupled with excellent collaboration and communication skills. </li><li>Track record of technical leadership, mentorship, and successful cross-functional collaboration in agile, dynamic environments. </li></ul> <h5>Benefits: </h5><ul> <li>Fully Remote work culture - we hire globally and all of our roles are fully remote! </li><li>Bring your own device stipend - buy your own laptop with funds from Andela </li><li>Flexible working hours </li><li>Equity (as a part of the compensation package) </li><li>Healthcare, 401k / pension </li><li>Generous Paid Time Off, Parental Leave, Compassionate Leave </li></ul> And more to come! We're on a journey to reimagine global benefits at Andela. At Andela, we outcompete through diversity. We know that our strengths lie in the multiplicity of talents, perspectives, backgrounds & orientations resident in our community and we take pride in that. Andela is committed to a work environment in which all individuals are treated with respect and dignity. Each individual has the right to work in a professional atmosphere that promotes equal employment opportunities and prohibits discriminatory practices. Andela provides equal employment opportunities and workplace to all employees and applicants without regard to factors including but not limited to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, pregnancy (including breastfeeding), genetic information, HIV/AIDS or any other medical status, family or parental status, marital status, amnesty or status as a covered veteran in accordance with applicable federal, state and local laws. This commitment applies to all terms and conditions of employment, including but not limited to hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Our policies expressly prohibit any form of harassment and/or discrimination as stated above. Andela is home for all, come as you are.

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

Andela Interview Questions

Andela Interview Guides