Hopper Senior Data Scientist at Hopper owns analytical relationships with partners and builds scalable reporting and machine learning solutions. The role focuses on enabling partners to make better decisions through data insights and automation.
Responsibilities
Hopper Technology Solutions (HTS) powers travel for some of the world's largest brands through our APIs, white label products and fintech, and every dataset we hold tells a story our partners need told clearly, repeatedly and fast. As a Senior Data Scientist on HTS you will own the analytical relationship with a portfolio of partners and build the machinery that answers their questions about supply health, pricing competitiveness and performance once, then answers them every week. You will be judged less on the sophistication of your models than on whether the people you work with, inside Hopper and across partner commercial teams, make better decisions because of you.
What would your day-to-day look like
Own the analytical relationship with a portfolio of HTS partners, working alongside Partner Success and Commercial to understand what they need, deliver it, and present it directly to their teams
Design and templatize repeatable reporting on supply health, pricing competitiveness and tenant performance that scales across hotels, flights, homes and cars without being rebuilt each time
Use AI assistants, agents and code generation tools as everyday collaborators to automate the repetitive parts of analysis and reporting, and help the broader team adopt the same practices
Frame and run exploratory analyses that deepen our understanding of partner and traveler behavior, and translate what you find into clear recommendations for product, engineering and business leaders
Apply machine learning and statistical methods to large, messy datasets where they move the needle, and recognize when a simple, well explained metric is the better answer
Build dashboards and self serve reporting in BigQuery, Looker, Tableau, Amplitude and Google Sheets, with an eye toward making yourself unnecessary for routine questions
Partner closely with product and engineering so the data we collect supports the questions our partners will ask next
An ideal candidate has
A degree in Math, Statistics, Computer Science, Engineering, Economics or another quantitative discipline, or equivalent practical experience
Extremely strong analytical and problem solving skills, with a track record of answering hard questions with data across more than one domain or product line
Qualification
Degree in MathFluency in Python and Pandas or R
Required
Degree in Math, Statistics, Computer Science, Engineering, Economics or equivalent experience
Strong analytical and problem-solving skills
Ability to communicate complex technical work to non-technical audiences
Comfort presenting findings and building trust with commercial counterparts
Deep SQL skills and experience with modern data warehouses like BigQuery
Fluency in Python and Pandas or R
Hands-on daily use of LLM-based tools such as Claude, ChatGPT, Cursor or Copilot
