Fill out the application form below.
Complete the quiz and online interview.
Upon passing these steps, you will be onboarded.
Start working on two data science projects from our platform.
Finally, you will be matched with a company to work on a real data science project.
In this project, you are tasked with predicting department-wide sales for 45 stores located in various regions using historical sales data. The objective is to develop an accurate predictive model for forecasting retail sales. Leveraging three key datasets—the Stores dataset, Sales Prediction dataset, and Features dataset—we aim to utilize historical sales data and additional relevant features to predict future department-wide sales accurately.
This project is a Customer Segmentation Analysis for a bank, which requires the use of the RFM Model. RFM - Recency, Frequency, Monetary based approach which evaluates customers on their purchase history to group them into segments. This analysis will assist the company in understanding which customer segments should be targeted to optimize sales revenue.
The objective of this project is to utilize time series analysis techniques to predict the stock prices of Tesla, one of the leading companies in the electric vehicle industry. Tesla's stock prices have exhibited considerable volatility and have attracted substantial attention from investors and analysts alike. By accurately predicting the future stock prices of Tesla, we aim to assist investors in making informed decisions and better understand the underlying trends and patterns in the stock market.
In today's data-driven business landscape, extracting valuable insights from data is paramount for making informed decisions. In the hospitality and food service sector, where customer preferences and market dynamics are constantly evolving, understanding patterns and trends within restaurant sales data is indispensable. Through careful analysis and visualization, we aim to uncover the hidden narratives, trends, and opportunities within this data, using two primary datasets: one capturing restaurant-level information and another detailing the specifics of products sold within these establishments.
The main objective of this project is to analyze sales data from Allforall, an e-commerce company specializing in electronic items. By studying sales trends, metrics, and patterns, the goal is to generate valuable insights that will help the company set strategic targets and improve its overall sales strategy. The data analyst will identify top-performing and underperforming products, address sales challenges, explore market opportunities, and pinpoint revenue-generating sales activities.
The main objective of this project is to gain deep insights into customer purchasing habits by utilizing the RFM analysis technique. By calculating RFM metrics, we aim to segment customers based on their purchasing behavior, allowing us to identify distinct groups with specific needs and preferences. This analysis will enable us to understand customer loyalty, identify high-value customers, and tailor marketing strategies to improve overall customer retention and satisfaction.
The primary objective of this project is to analyze customer-level data from a prominent telecom firm and develop predictive models to identify customers at high risk of churn. By leveraging data-driven insights, the project aims to empower the telecom company to proactively reduce customer churn, enhance customer retention, and prioritize efforts towards retaining high-profitable customers.
The objective of this project is to build an effective fraud detection system for financial transactions using machine learning techniques. By analyzing the provided dataset of simulated mobile money transactions, we aim to detect and prevent fraudulent activities, safeguarding the financial interests of the company and its customers.
Working on project's at Leveragai enhanced my technical skills and provided invaluable insight into the real-world data analytics projects. This experience built a strong foundation of trending technical knowledge and boosted my confidence.
Huntington National Bank
Collaborating with Leveragai has been an incredible experience. Together, we are creating a groundbreaking, AI-driven training platform that is set to revolutionize the future of learning. As a data scientist, I am thrilled to be part of this innovative journey.
Morgan Stanley
Leveragai's Experience Building Program was a game-changer for me. The one-on-one guidance felt like having a mentor by my side, and the resume help gave me the extra edge I needed. Thanks to Leveragai, I now approach data in a whole new light, ready to take on any challenge.
Graduate Student, UMBC
My experience at Leveragai is proving invaluable in my development as a data scientist. The projects provide me with the opportunity to delve into real-world applications. I am much more confident in achieving my goals now.
Graduate Student, UMBC
Working at Leveragai has been an invaluable experience. The opportunity to work with one of their clients, Hedged Capital, not only allowed me to grow my technical skills but also helped me improve my client management and project management skills.
Graduate Student, UMBC
Leveragai’s Talent Experience Building Program is a unique opportunity for data science enthusiasts to gain real-world experience through hands-on projects and collaboration with companies seeking data-driven solutions. Participants work on two industry-specific projects to enhance their skills and prepare for their dream job.
The Experience Building Program is at least a 3-month program that offers empowering participants to thrive in the dynamic field of data science. However, it can be extended up to 9 months in 3-month increments.
The program is open to data science enthusiasts with varying levels of experience. Whether you are a beginner or an experienced data scientist looking to level up your skills, you are welcome to apply.
To apply for the program, simply visit our website and fill out the application form. Once submitted, our team will review your application and get in touch with you for the next steps.
You need to pass the online assessment before starting the Program, which consists of python-based coding questions and a multi-choice machine learning questionnaire. This will be a 1-hour assessment. Once you pass this step, you will have an online interview with one of our team members. Expect machine learning models questions during this half-hour interview. When you clear these two stages, you will be ready to work on the projects. This step is completely free.
Leveragai believes in accessibility and opportunity for all. Therefore, the Program has a one-time fee of $500. Irrespective of the time you spend in the Program, you just pay this one-time fee.
Participants receive continuous support and guidance from experienced data scientists and mentors throughout the program. Our team is dedicated to helping you achieve your goals and excel in your data science journey. You can consult them when you get stuck while working on your projects.
Upon completing the program, participants gain valuable experience, industry connections, and the potential to secure internships or even full-time positions with our partner companies.
Yes, if you are not matched with a company after you completed the two assigned projects, Leveragai pays your money back.