Data analysis for gig-economy work prediction

Hi!

So, my team and I are working on a new service which helps gig-economy workers (e.g. Uber-drivers, food-delivery, etc.) to maximize their income by using predictive analytics. We want to be able to tell them where to be when to make the best money while working for Uber (using predictive analytics). We also want to combine this with other services (like food-delivery, or other tasks) so these workers can combine their gig jobs intelligently (for example: only work for Uber when demand is high, and when ride-sharing demand is low, offer alternatives where demand is high, for example food-delivery).

At the moment we have publicly available data from New York City taxi rides of the year 2013 and we'd like to combine this data with other sets of data to find correlations (e.g. weather, events, tourism, etc.). After that, we need to combine these correlations with open APIs (again, like weather-forecast or events) and apply them to the City of London in order to make first small predictions.

My team and I have quite little experience in data analytics and software development so we don't know how complex these tasks are. We have a budget for this project and we'd love to work alongside a talented data scientist / programmer to create this prototype.

If you have any questions or if you are interested in this job, please send me a message and I'll get in touch with you asap to discuss further details.

Thanks and see you soon!
Christoph & team