Simulation details

Uber, a global leader in ride-hailing, operates in over 900 metropolitan areas worldwide, with a significant presence in tier-1 US cities like New York, Los Angeles, Chicago, and San Francisco. The company uses the vast amount of data generated from its operations to make strategic decisions. However, the Covid-19 pandemic has significantly impacted Uber’s operations, leading to a shift in focus towards food delivery. Despite stiff competition from other players like Lyft and Ola, Uber maintains a 60% market share in tier-1 US cities.

Your challenge is to build a model to forecast future demand for Uber’s services in tier-1 US cities. These cities present unique challenges in predicting ride demand due to their dense populations, dynamic economies, and intricate transportation networks. The demand for rides is influenced by various factors, including the time of day, weather conditions, local events, and the availability of public transportation. Your task of predicting demand has real-world implications that can significantly impact Uber’s operations.

As a Data Scientist at Uber, your mission is to analyze the ride data structure and develop a predictive model for future demand in tier-1 US cities. You will interact with various stakeholders, including Dr. Emily Chen from the Data Science team for data gathering and model development, Mr. Robert Johnson from the Operations team for understanding budget constraints and city prioritization, and Ms. Laura Smith from the Executive team to present the final recommendations.

Your action plan will begin with gathering data and understanding historical patterns talking to other team members. You will then use this information to develop a predictive model for future demand. Finally, you will make recommendations based on your forecast, considering factors such as budget constraints and city prioritization.

This simulation will allow you to apply and develop key skills relevant to a Data Scientist role. You will use Data Analysis skills to interpret historical ride data, Predictive Modeling techniques to forecast future demand, and Decision-Making abilities to recommend strategic actions.

How to complete the simulation

To successfully complete this simulation, you need to produce three key deliverables:
1. A comprehensive data analysis report detailing the patterns and information gathered from the team and data model.
2. A predictive model proposal for future demand, based on the current data structure and historical information obtained from the team.
3. A strategic recommendation report outlining which cities to prioritize and how to allocate resources effectively.

The predictive model should have a target margin of error within 10%. Your recommendations should be accepted by the Executive team. Compile these deliverables into a single PDF and submit it to your manager, Mr. James Brown.

Team

Who you will work with in this Simulation
Your team is 100% generated by AI – you will not interact with real people and no human will read your conversation.

James Brown
Senior Data Scientist

Emily Chen
Data Science Team Lead

Robert Johnson
Operations Manager

Laura Smith
Chief Strategy Officer

Alex Taylor
Data Scientist

Organization

Uber Technologies Inc. is a global technology company based in San Francisco, California, primarily known for its ride-hailing app that connects passengers with drivers of vehicles for hire. In addition to ride-sharing, Uber’s business extends to food delivery through Uber Eats, package delivery, and freight transportation services. The company operates in over 900 metropolitan areas worldwide and utilizes a dynamic pricing model based on supply and demand. Uber has significantly influenced urban transportation and has been a key player in the gig economy, offering flexible job opportunities but also facing regulatory and labor challenges. Its platform features also include carpooling options, safety features, and various vehicle choices to cater to different consumer needs.