At Hopper, we’re on a mission to build the most customer-centric travel company on earth. We are leveraging the power that comes from combining massive amounts of data and machine learning to build the world’s fastest-growing mobile first travel marketplace -- one that enables our customers to save money and travel better.
Hopper’s goal is to reduce traveler anxiety throughout all stages of the trip buying and taking process. By creating a transparent travel marketplace and unique, data-driven financial technology products focused on providing peace-of-mind, Hopper adds value along each step of the customer’s journey.
Hopper has launched several bespoke fintech products that leverage our immense first and third-party data to create products and value that do not exist elsewhere - including Refundable and Flexible Tickets and Price Freeze. Thanks to these offerings, Hopper’s revenue growth is up 112% despite the travel slowdown due to COVID-19.
With over $250M CAD in funding from leading investors in both Canada and the US, Hopper is primed to continue its acceleration to becoming the world’s fastest-growing end-to-end customer-centric travel offering.
Recognized as one of the world’s most innovative companies by Fast Company three years in a row, Hopper has been downloaded over 50 million times and sees over 1 million new installs per month. The app has received high praise in the form of mobile accolades such as the Webby Award for Best Travel App of 2019.
Come take off with us!
We’re looking for a data science intern to audit data integrity, document the data architecture and drive data quality improvements in collaboration with our backend infrastructure engineers. Because 80% of data science work involves data wrangling, this work improves the analytical correctness and velocity of all data scientists.
This includes systematically documenting a data science dictionary that solves problems like:
Knowing which table and field has the data you’re looking for
Knowing the technical definition and vernacular definition of the data field
Knowing the direct source or calculation behind the data field
Knowing which fields can be used to join which tables
Identifying variables that have different names in several different tables
Identifying variables that have the same name in different tables but give different values
Identifying variables that are foundational to the business but are currently missing
Finding violations of assumed 1:1, 1:many, many:1 relationships
Finding anomalies in distribution over time
Finding anomalies in crosstab distributions
Finding incorrect duplications of entire row records within the table or incorrect duplications of certain fields
This person would also work with Engineering to communicate mistakes found, recommend changes, create SQL views of “gold standard sources of truth” and track Engineering corrections over time.
They would create a Data Science version of an Entity Relationship Diagram (e.g. what are the “quantum” units of different “objects” like clients, app events, policies and requests and how they all relate to each other).
They would document idealized business metrics, what proxy metrics currently exist and what new metrics could be created to bridge the gap.
Leveraging data science to solve customers’ problems is in Hopper’s DNA. While this began with helping customers plan and book travel more intelligently, we have extended our capabilities to ensure customers secure the best fares for their trips with unique product offerings like Price Freeze. These product offerings currently make a large fraction of Hopper’s revenue and have helped drive Hopper’s growth despite the travel slowdown due to COVID-19.
Customers that book with Hopper and use Price Freeze can be confident they won’t miss out on a great deal. Price Freeze lets customers that are not yet ready to book lock in great flight deals at a low cost for anywhere from 12 hours to 14 days.
An Ideal Candidate Has
A degree in Math, Statistics, Computer Science, Engineering or other quantitative disciplines
At least 1 year’s academic or practical experience in SQL
At least 1 year’s academic or practical experience in data wrangling with Python Pandas
The ability to write code in both SQL and Python for common data wrangling tasks
Excellent written communication skills with a non-technical writing sample required
Any of the following experiences would be nice to have but not required: git, BigQuery, Jupyter, handling JSONs, experience with extremely large datasets
More About Hopper
Hopper is valued at $3.5bn making us the 5th most valuable travel business in the world. We’re best known as a travel app and we just raised a further $175m from a funding round led by GPI Capital. Our investors also include Goldman Sachs and Capital One for whom we exclusively power their travel portal.
At Hopper, we’re on a mission to build the most customer-centric travel company on earth. We are leveraging the power that comes from combining massive amounts of data and machine learning to build the world’s fastest-growing mobile first travel marketplace - one that enables our customers to save money and travel better. It’s cheaper to purchase travel with Hopper!
Hopper’s goal is to reduce traveler anxiety throughout all stages of the trip buying process. By creating a transparent travel marketplace and unique, data-driven financial technology products focused on providing peace-of-mind, Hopper adds value along each step of the customer’s journey.
Recognized as one of the world’s most innovative companies by Fast Company three years in a row, Hopper has been downloaded over 60 million times and sees over 1.9 million new installs per month. Our revenue growth is up 330% versus 2020 as we continue to outperform market recovery .
Now we're laying the groundwork for continued expansion in 2021 by adding great people to our team who can help us compete with the travel giants. Come take off with us!