Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. It takes a unified team committed to our core values to achieve this goal. Airbnb's various functions embody the company's innovative spirit and our fast-moving team is committed to leading as a 21st century company.
About the position:
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 Science Leader 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.
Some of the challenges you will face include:
- 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.
Here are example traits we value:
- Advanced degree in a quantitative field. PhD is a plus.
- 12+ years industry experience developing machine learning models at scale from inception to business impact. Leadership opportunities are also available.
- Demonstrated ability to create and drive technical and impactful roadmaps for the business, and lead seamless execution of it.
- Proven ability to tailor your solutions to business problems in a cross functional team.
- Deep understanding of modern machine learning techniques and their mathematical underpinning, such as classification, clustering, optimization, deep neural network and natural language processing.
- Strong programming skills (Python, R preferred).
- Ability to communicate clearly and effectively to cross functional partners of varying technical levels.
- Data analytical and data engineering experience is a plus (Hive, Presto, Spark preferred).
- Experience productionizing real-time machine learning models is a plus.