Data scientists support Product and Engineering teams by designing experiments, implementing predictive models, and mathematically optimising resources. We also build computer vision and natural language processing applications. We have substantial internal tooling from the Model Lifecycle team to allow data scientists to gather and label data and train and deploy the models on a global scale.
Capturing credit card fraud is an uphill battle of fraudsters constantly adapting and using stolen credit cards to find ways to get free rides. However, our fraud rules engine and machine learning models can capture such instances quickly, keeping costs down for honest users.
As Bolt is a micromobility provider offering scooter and e-bike rentals across Europe, this opens up a variety of interesting technical challenges. The diverse Rentals Data Science team consists of IoT specialists, mathematical optimisation gurus, and deep learning enthusiasts.
The Tools and Workflows (T&W) team optimises different workflows inside Bolt. Whether helping our Customer Support team handle incoming user requests more efficiently or enabling our mapping specialists to improve our routes and maps, the T&W team has the right tools for the job.
The main product is the Verification Platform, which we use to verify the identity of Bolt users and driver partners by checking the data from the documents they provide. Afterwards, we match the face of the person to the document and make sure that both the face and the document are first-hand captures belonging to the real person.
The food delivery marketplace is even more complex than ride-hailing due to having an additional third party — the food providers. The dedicated Delivery team owns a wide array of projects that use some elements from ride-hailing, such as dynamic pricing, dispatch optimisation, travel and cooking time prediction, and user food recommendations.
Our main goal is to improve the experience of our ride-hailing users during the pick-up/drop-off selection. To achieve this, we use an NLP model for correcting user queries and a Learning to rank (LTR) model for providing the most relevant results given the context. We use unsupervised learning to see missing or erroneous places of interest, ensuring that our underlying data has good coverage and is up-to-date.
We’re optimising for the accuracy of duration and route predictions powering the dispatching engine used in ride-hailing and food delivery. We use a combination of a traditional graph-based router and a machine-learning layer. We work on various projects, including travel time prediction, traffic modelling, inferring missing map elements, and finding route-optimal pick-up/drop-off coordinate perturbations.
The Campaigns (Rides) team works on optimising global campaign budgets at micro (user) and macro (market) levels. On a micro level, we work on LTV, retention, churn modelling, and developing novel campaign targeting methods. Long-term market modelling for investment strategy determines the macro level spend optimisation. Here, we deal extensively with multivariate time series forecasting of various marketplace and financial indicators.
The team's main objective is optimising ride-hailing marketplace efficiency. Our flagship product is dynamic pricing which balances demand and supply. Another focus area is order dispatching. These algorithms maximise order completion rates over different rider and driver matches. In addition, we work on research topics such as agent-based marketplace simulation, structural economic modelling, and causal inference that help us test, validate, and explore new optimisation areas.