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Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.
The Community You Will Join:
You will join the Payments Data Science organization, which sits at the intersection of Trust and Payments and powers the systems that move money safely and efficiently across Airbnb's global marketplace. The team spans payment optimization for guests and hosts, fraud and risk mitigation, complex measurement, and regulatory compliance. We partner directly with Payments product and engineering leadership, Finance, and Trust to ensure every transaction is fast, safe, and compliant at global scale. Our work directly shapes decisions made by senior leaders, including Payments executive leadership, and requires a rigorous, evidence-based approach to every recommendation we make. Our Data Science team enables this mission by providing reliable measurement frameworks to deliver robust data insights, build and enable state-of-the-art data products/models, and provide actionable and reliable business guidance.
The Difference You Will Make:
We are looking for a passionate data scientist to lead quantitative measurement efforts and bring novel scientific approaches to drive decision making across our platform’s payment experience. This data scientist will perform careful hypothesis generation, causal inference framework development, and model development/evaluation to ideate and drive payment strategies on our platform. This role will have a particular focus on payments fraud mitigation and loss optimization, with the goal of making our platform safer for our community.
Our Data Scientists have a deep understanding of causal framework development, statistical analysis, machine learning model development and evaluation strategies, and the complications of running experiment/quasi-experimental methods in a two-sided marketplace. They have keen business sense and are able to develop novel solutions to fraud and risk problems that don't have an established playbook and utilize their findings to communicate across a wide range of partners to drive our data & product roadmaps. They are not only the trusted data expert on their team, but also a storyteller.
Examples of projects you may work on include, development of novel metrics and frameworks that can efficiently measure outcomes (often balancing competing tradeoffs), generating deep root cause investigations and long term impact measurements, and building/evaluating ML and agentic models to optimize guest, host, and business outcomes.
A Typical Day:
- Inference: Develop and apply causal inference methods, including experimental, econometric regressions, and quasi-experimental methods to measure a wide-range of platform/product impacts.
- AI/ML: Build methods for robust evaluation of ML/AI model efficiency and performance. Ability to identify use-cases for and develop predictive models to classify, segment, and interpret our users’ behavior. Support evaluation and optimization of agentic and LLM-based systems.
- Optimization: Develop methodologies to explore/simulate the impact of new interventions and develop data products to optimize product/operational strategies.
- Communication: Deliver robust research reports and effective data visualizations. Collaborate with and present to stakeholders to identify opportunities and communicate findings, and drive impact.
- Empowerment: Think strategically about opportunities to improve and scale our brand measurement and customer insights.
Your Expertise:
- 5+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (math / economics / statistics, and etc.), or 3+ years of experience with a Phd degree.
- Strong knowledge of causal inference, experimentation, applied statistical modeling, and end-to-end ML development.
- Skilled in statistical programming (Python or R) and database usage (SQL)
- Demonstrated track record of owning a business or technical domain end-to-end at a prior company: setting your own roadmap, being the accountable expert others escalate to, and driving a problem to resolution.
- Proven ability to communicate clearly and effectively to audiences of varying technical levels
- Ability to work independently, set your own roadmap, and drive cross-functional alignment
- Payments Fraud/Risk Domain expertise is a strong plus.
- Familiarity with evaluating agentic or LLM-based systems (e.g., decision-quality measurement, human-in-the-loop calibration) is a plus.
Your Location:
This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from.
Our Commitment To Inclusion & Belonging:
Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.
We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: [email protected]. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process.
We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application.
Equal Employment Opportunity:
Airbnb values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Airbnb are considered without regard to race, color, religion, national origin, age, gender, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, gender expression, sexual orientation, or any other legally protected characteristic.
How We'll Take Care of You:
Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.
Reasonable Accommodations: We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
A Note on Recruiting Scams: Scammers sometimes pose as Airbnb recruiters to get money or personal information from candidates. A few things that will always be true for Airbnb’s hiring process: our open roles are posted on Airbnb’s Career’s Page at careers.airbnb.com, and our recruiters correspond only from @airbnb.com or @ext.airbnb.com email addresses. Our recruiters will never ask for your Social Security number, bank account details, passport, or payment app information while you are interviewing. We’ll also never ask you to pay a fee, send money, deposit or cash a check, or purchase work-related equipment (such as a company laptop) during the interview process. We encourage candidates to remain vigilant of these recruiting scams and not share sensitive information if you do not believe an individual is actually affiliated with Airbnb.
Skills Required
- 5+ years industry experience with a Master’s degree in a quantitative field, or 3+ years with a PhD.
- Strong knowledge of causal inference, experimentation, and applied statistical modeling.
- End-to-end machine learning development and evaluation experience.
- Skilled in statistical programming (Python or R) and database usage (SQL).
- Demonstrated track record owning a business or technical domain end-to-end.
- Proven ability to communicate clearly and effectively to varying technical audiences.
- Payments fraud/risk domain expertise.
- Familiarity with evaluating agentic or LLM-based systems (decision-quality measurement, human-in-the-loop).
Airbnb Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Airbnb and has not been reviewed or approved by Airbnb.
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Fair & Transparent Compensation — Pay ranges and total compensation components are increasingly disclosed, and country-level pay practices avoid location-based cuts, which feedback suggests improves consistency and perceived fairness.
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Healthcare Strength — Comprehensive core health coverage for employees, mental health resources, and substantial dependent support are repeatedly highlighted as robust and a standout element of the package.
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Leave & Time Off Breadth — Generous PTO, a winter shutdown, sabbaticals, and paid volunteer time are described as extensive, with parental leave and phased return options reinforcing time-off flexibility.
Airbnb Insights
What We Do
Airbnb is a community based on connection and belonging—a community that was born in 2008 when two hosts welcomed three guests to their San Francisco home, and has since grown to 4 million hosts who have welcomed over 800 million guest arrivals to about 100,000 cities in almost every country and region across the globe. Hosts on Airbnb are everyday people who share their worlds to provide guests with the feeling of connection and being at home. At Airbnb, we believe that hosts, guests and the communities where we operate are all stakeholders we have a responsibility to serve, and that by serving them alongside our employees and investors, we will build an enduringly successful company.
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