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Senior Data Scientist- Travel Tech

AI Summary
Hopper is seeking a Senior Data Scientist to own analytics relationships with HTS travel-tech partners and turn partner questions into scalable reporting products. The role combines SQL, Python, statistical analysis, machine learning, dashboards, and automated or LLM-driven workflows across travel supply, pricing, and performance data. The scientist will present recommendations directly to external commercial teams and collaborate closely with product and engineering on data collection. Success depends on simplifying complex analyses into trusted narratives and self-service tools that reduce recurring manual work. This is a senior, highly partner-facing role in a fast-moving B2B travel platform environment.
Remote from
USA
Salary
USD 160k–210k / yr
Employment
Full Time
Experience
Director
Published
Apply before
8 Dec 2026
Listing views
32
Application actions
1
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Opportunity details

About this role.

About the Role

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

  • A proven ability to turn complex technical work into simple, credible narratives for non technical and executive audiences, including external partners

  • Comfort being partner facing, presenting findings, fielding tough questions live and building trust with commercial counterparts

  • Deep SQL skills and experience with modern data warehouses such as BigQuery, plus fluency in Python and Pandas or R for large scale data preparation and analysis

  • Hands on, daily use of LLM based tools such as Claude, ChatGPT, Cursor or Copilot for writing code, SQL, analysis and documentation, and a point of view on where they help and where they fall short

  • Experience building or deploying automated and agentic workflows, from scheduled pipelines to LLM driven report generation and prompt or skill libraries, that turn one off analyses into repeatable products

  • Experience with data mining, machine learning and statistical modeling, and the judgment to know when to reach for them

  • Experience with business reporting and experimentation tools such as Tableau, Looker or Amplitude

  • A strong bias toward simplifying, templatizing and shipping over polishing

  • Travel industry experience in lodging, air, vacation rentals or car rental, or in a marketplace, B2B platform or API distribution business, is a plus

  • Experience with geographic data in search, filtering or recommendation, or working with external customers in a consulting, solutions or customer analytics capacity, is a plus

Perks and benefits of working with us

  • Well-funded and proven startup with large ambitions, competitive salary and the upsides of pre-IPO equity packages

  • Unlimited PTO

  • Carrot Cash travel stipend

  • Access to co-working space on demand through FlexDesk AND Work-from-home stipend

  • Please ask us about our very generous parental leave, much above industry standards!

  • Entrepreneurial culture where pushing limits and taking risks is everyday business

  • Open communication with management and company leadership

  • Small, dynamic teams = massive impact

  • 100% employer paid Medical, Dental and Vision coverage for employees

  • Access to Disability & Life insurance

  • Health Reimbursement Account (HRA)

  • DCA/ FSA and access to 401k plan

About Hopper

At Hopper, we’re building the leading travel technology platform in the world, and we’re doing it by solving real problems for real travelers globally. Our Hopper consumer booking platforms and HTS (Hopper Technology Solutions), our B2B business, work together to give travelers something the industry has never reliably offered: transparency, flexibility, and real savings on every trip. We combine massive amounts of data and AI with proprietary fintech products to make that happen, not just another booking engine, but a fundamentally better way to plan and experience travel for our customers and our partners. We are proof that better-for-the-customer and better-for-the-business can be one in the same

And we’re continuing to grow fast! The Hopper app has been downloaded over 120 million times, with 70% of our users being Gen Z and Millennials, next-generation travelers. Today, our biggest opportunity is powering travel fintech, commerce, and loyalty for some of the biggest brands in the industry. Our first HTS partnership with Capital One helped launch a dedicated travel portal for a major financial brand’s cardholders, and we’ve since expanded across some of the biggest names in travel and banking, including Air Canada, Uber, Royal Bank of Canada (RBC), and Nubank, powering everything from a single fintech integration to a full end-to-end travel loyalty portal.

That’s our trajectory: we started as a small, disruptive app within the travel space, and now we’re the engine under the hood powering how Travel Tech works.

What that looks like in practice:

  • Billions of dollars in travel and travel fintech move through Hopper and HTS every year.

  • Our fintech products, including Cancel for Any Reason and Disruption Assistance for Any Reason, post exceptionally strong customer satisfaction because the terms are clear and resolutions are instant, no questions asked.

  • Nearly 30% of app customers add a fintech product to their booking, and those customers are 1.6x more likely to book with us again.

  • HTS now drives more than 90% of the business, proof of how fast B2B has scaled.

  • Through HTS, any travel provider (airlines, hotels, banks, travel agencies) can plug the company’s fintech, commerce and AI solutions into their own channels, whether that’s a single integration or a full end-to-end travel portal.

Come take off with us!

#LI-Remote

While this role utilizes automated systems to assess candidate suitability, every actionable decision is made by a human member of our team. Under local regulations, you have the right to understand the principal factors used in this assessment, request a correction of your data, or request a human review of any automated decision. To exercise these rights, please contact recruitment@hopper.com

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightРоль требует одновременно глубокой аналитической экспертизы, навыков автоматизации и уверенной работы с внешними партнёрами. Необходимо самостоятельно выбирать подходящие методы и превращать сложные данные в масштабируемые бизнес-решения.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$185,000
US market range (per year)$160k–$220k
AI insightУказанная работодателем годовая базовая зарплата составляет $160,000–$210,000 USD, медиана диапазона — $185,000. Оценочный рыночный диапазон для senior data scientist с сильной партнёрской, BI и ML-специализацией на удалённом рынке США составляет $160,000–$220,000 USD в год; возможны дополнительные equity и льготы.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a scalable reporting framework for supply health and pricing competitiveness across multiple travel partners?

I would first define a shared metric layer with consistent business definitions, dimensions, and data-quality checks. I would build reusable SQL models and scheduled transformations in the warehouse, then expose partner-specific views through parameterized dashboards and automated report templates. I would validate the metrics with commercial stakeholders and document assumptions so the framework remains trusted as it scales.

Describe how you decide whether to use a machine-learning model or a simpler metric-based analysis.

I start with the decision the business needs to make and the cost of being wrong. If a transparent metric or segmented analysis answers the question reliably, I prefer it because it is easier to explain and maintain. I use machine learning when it offers measurable incremental value, such as improved prediction, ranking, or anomaly detection, and when data quality, monitoring, and operational ownership support it.

How would you explain an unexpected decline in conversion to a non-technical external partner?

I would lead with the business impact, the affected segments, and the strongest evidence rather than technical methodology. I would explain the likely drivers using clear visuals and comparisons, distinguish facts from hypotheses, and recommend specific next actions. I would also state what additional data or experiment would confirm the diagnosis.

What is your approach to using LLM tools in analytics and reporting workflows?

I use LLM tools to accelerate drafting SQL, documentation, code scaffolding, report narratives, and repetitive classification tasks, while maintaining human review for logic, privacy, and accuracy. For production workflows, I define evaluation criteria, version prompts and inputs, add validation checks, and monitor failure modes. The goal is to automate low-value repetition without obscuring analytical accountability.

How would you partner with product and engineering to improve future analytical capabilities?

I would translate recurring partner questions into concrete data requirements, event definitions, and instrumentation priorities. I would work with product and engineering early to clarify grain, identifiers, latency, and quality expectations, then validate new data against intended decisions. This makes analytics more proactive and reduces the need for costly retroactive data reconstruction.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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