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Staff Data Scientist - Algorithms, Trust

Airbnb was born in 2007 when two Hosts welcomed three guests to their San Francisco home, and has since grown to over 4 million Hosts who have welcomed more than 1 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:

Trust is at the foundation of every Airbnb experience and as such we aim to make it the most trusted platform in the world. To achieve such a goal, the Data Science team relies on a diverse collection of structured and unstructured data to design, build, and support machine learning models to detect and prevent potential negative experiences and fraud attempts.

As a Data Scientist working on Algorithms, Trust, you will have the opportunity to collaborate with a strong team of engineers, product managers, designers and operation agents to build scalable and robust systems to detect, prevent and mitigate fraud on Airbnb. You will be deeply involved in the technical details of building highly available and real-time risk detection services to understand ever evolving attack vectors and to keep Airbnb a safe and trusted community.

The Difference You Will Make:

As an ML Data Scientist in the Platform team you will work with a diverse collection of structured and unstructured data to design, build, and support machine learning models. You will collaborate with a strong team of engineers, product managers, designers and operation agents to incorporate these models into scalable and robust systems that enable exceptional product experiences.

The richness of Airbnb's data, the complexity of its marketplace and the variety innate in our product mean that we need to operate at the state of the art of Machine Learning practice. We are committed to investing in long term innovation to solve the complex problems we face, and to do that we need the very best experts in ML and AI to join us.

A Typical Day:

  • Building machine learning models to detect high risk activities like account takeovers, fake contents and fraudulent transactions, or high risk entities like fake accounts or stolen cards.
  • Experimentation of new Airbnb product features to deter and mitigate risk.
  • Working cross functionally with operations and product teams to define and collect labels for model training, optimize effectiveness of manual review, and build self-satisfiable verifications that scale.
  • Devising optimization models to make optimal business decisions while minimizing risk
  • Innovating modeling frameworks in this adversarial setup, e.g., how can models collaboratively surface more risks, or how can models adapt to emerging patterns quickly.
  • Building NLP models to detect spam and inappropriate content on the fly.
  • Utilizing Deep Learning techniques for advanced feature engineering and model building, e.g., how to model for user behaviors sequences, or how can we detect anomalies effectively.

Your Expertise:

  • Advanced degree in a quantitative field, e.g. Computer Science, Operations Research, Mathematics
  • 9+ years industry experience developing machine learning models at scale
  • Experience taking research prototypes to production.
  • Proven ability to design analytical solutions to complex business problems and to partner cross-functional peers to tailor those solutions for the greatest possible impact.
  • Deep understanding of ML domains (e.g. NLP, computer vision, recommendation, anomaly detection, graph learning), algorithms (eg. deep learning, gradient boosted trees, optimization), and best practices, (e.g. skew minimization, A/B testing, feature engineering, model selection)
  • Strong programming (Python / Scala / Java / C++ or equivalent) and data engineering skills
  • Utilize OSS ML technologies such as PyTorch, TensorFlow, Spark, Airflow
  • Versatility to communicate clearly with both technical and non-technical audiences.
  • Publications or presentations in recognized Machine Learning and Data Mining journals/conferences 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: reasonableaccommodations@airbnb.com. 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.

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.

Pay Range

$185,000—$245,000 USD
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CEO of Airbnb
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Brian Chesky
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Airbnb is a leading online marketplace for short- and long-term homestays and experiences founded in 2008. We provide access to 6+ million unique places to stay in more than 81,000 cities and 191 countries worldwide.

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DATE POSTED
June 3, 2023

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