# How Uber & Lyft Use Data Scientists and Data Engineers



## Maven (Feb 9, 2017)

*Data scientists and data engineers help Uber and Lyft extract value from their masses of data.*
By William Chen, Data Scientist at Quora, Updated Mar 3, 2015
_Note: I have no inside information and I do not work for either of these companies. These are just my personal ideas of how Uber and Lyft could use their data scientists and data engineers._

*Dynamic pricing models*

I believe that these are all driven by the data teams - this involves a careful tuning of dynamic pricing to make sure that the relationship between demand / supply is fairly constant even after huge events like New Years or some weekend concert.










*Location-based demand models*

Heatmaps that update throughout the day for where drivers should wait for riders can help both drivers accomplish more rides and riders wait less time for a car. Time spent driving to a rider is time that neither the driver nor rider likes, and it's best to minimize this as much as possible. If possible, it would be even better to make a _predictive_ heatmap, so drivers can know where to head and wait for passengers in advance.










*Driver dashboards*

To help drivers track and understand the income that they're generating and to better understand their historical performance so they can adjust their routes / schedules as necessary.

*Competition modeling*

To understand how well the competitor is doing in the same locale, and try to identify new cities that the ridesharing service can expand to / recreate previous success in.

*User acquisition, retention, and lifetime value*

To make sure that all the crazy referral bonuses paid will be beneficial in the long run, and to determine an upper bound for how much money the ridesharing service can spend to acquire customers. How much is a new user worth? A recurring user? How do we get users who haven't taken a ride in a while to come back to the app?

*User Engagement Modeling*

How bad is it exactly for a user to open up an app and see that there are no rides available? How bad of an experience is it to encounter surge pricing? How bad of a signal is a user opening the app and then immediately closing it? How do these behaviors affect downstream user engagement?

*Product Metrics*

What are the main metrics that we should be tracking daily? What about conversion rate? Daily active users? Virality? Referral rates? Surge pricing data? Driver availability?

*A/B Testing*

Is it better to show surge pricing at 1.5x or 50%? Is it better to show surge pricing only after the user clicks on a request button? What is the effect of subtracting 1 minute from all the arrival times? What is the effect of showing more or fewer cars on the screen? Should we keep this new feature?

*Internal Tool Building and Data Reliability*

To help the company better track and understand growth and encourage a more data-driven approach to decision making, while making sure that everything is reliable and accurate. There's a lot of data that needs to be logged, moved around, stored, and extracted and everything better be trustworthy and accurate.

*Exploratory Analysis, Deep Dives, Strategic Research*


Understand the use cases of their product, and to see if they can identify recurring patterns of use like commuters, or people picking up their kids from schools
Understanding virality and how invite dynamics work. How can Uber/Lyft increase the viral referral cycle while still remaining within a reasonable budget?
Understanding recruitment of drivers, and understanding motivations for drivers. What kinds of drivers are more likely to become long-term value-adders to the system?
Understand possible points of inefficiency and how to address them.
Understand what markets to expand to next!
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More about Uber engineering at
https://uberpeople.net/threads/uber-updates-question.160891/


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## Mars Troll Number 4 (Oct 30, 2015)

But... it doesn't take a genius to see trends.

If yo know the city you live in it's not that hard to figure out.


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## AVLien (Mar 4, 2017)

I am actually trying to learn R Data currently to predict surges. In theory, ifi can aggregate weather conditions, historic surge data, & local events i believe I can extrapolate future surges with decent accuracy.

Also, learning to wrangle "big data" isnt a useless skill on a resume. 

If anyone would be interested in nerding up this project with me, let me know. We could probably use their tricks against them somewhat.



Mears Troll Number 4 said:


> But... it doesn't take a genius to see trends.
> 
> If yo know the city you live in it's not that hard to figure out.


You're talking about hundreds of data points times millions of people compounded by a fair amount of random factors. If it were that easy, we'd all be about to retire by now.

You can guess that, if there is a big sports event, people will want rides to & from it. Maybe people will want to go out to dinner around 7:30 on weeknights, but what about the nights you go out expecting a big surge & it just doesn't happen?

Predicting those kinds of things could save drivers tons of wasted gas & time.


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## everythingsuber (Sep 29, 2015)

Mears Troll Number 4 said:


> But... it doesn't take a genius to see trends.
> 
> If yo know the city you live in it's not that hard to figure out.


Mostly this. I know pretty well exactly how the city I work in going to move probably 2 weeks in advance. Idiots out there dumb themselves down to the level of a computer. The computer knows nothing any half decent driver doesn't know but put that knowledge in in a computer and suddenly people think magic.


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## Mars Troll Number 4 (Oct 30, 2015)

That and thins like printing a concert schedule, sporting team schedules, (theme park hours for me..) convention schedules...

I can predict good and bad taxi shifts days in advance here..

The cab company writes up this nice neat little summary of all this and hands them out,

Also has hotel check-in check-out info as well...


d


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## sanchez15 (May 16, 2016)

AVLien said:


> You can guess that, if there is a big sports event, people will want rides to & from it. Maybe people will want to go out to dinner around 7:30 on weeknights, but what about the nights you go out expecting a big surge & it just doesn't happen?
> 
> Predicting those kinds of things could save drivers tons of wasted gas & time.


That just means Uber brought on too many too many drivers, gave out too many sub-prime car loans, and now everyone is ****ed.


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