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Listing Understanding Data Science develops a holistic understanding of listing attributes to enable better product experiences for guests and hosts. The team is focussed on using Computer Vision (CV) to understand the rich information available in photos and incorporating photos in a multimodal approach to listing understanding. You will work cross functionally with tech, product, and design organizations to develop and execute a roadmap for how photos are managed by hosts and enrich the guest experience.
Job Responsibility:
Size business opportunities and develop the roadmap for investments in CV and multimodal listing understanding and integration into product experiences
Leverage photos and related structured and unstructured data to build and continuously improve listing understanding
Build causal models to understand how photos impact guest search and drive bookings and trip quality
Stay up to date with SoT models and prototype machine learning product features and iterate with engineering, product, and design
Develop and implement assessment frameworks for CV and multimodal modeling improvements to ensure that they are safe and effective
Measure the business value of our work through experimentation and causal analysis
Requirements:
9+ years of industry experience in applied Machine Learning, with experience in Computer Vision, preferably including CLIP and ViT
Causal inference expertise, preferably with marketplace experience
Advanced degree in Computer Science, Statistics, Econometrics or related field
Strong in XFN communication with partners in product, engineering, and design
Expert in at least one programming language for data analysis (Python or R) with familiarity in SQL
Comfort with developing proof-of-concept prototypes
Passionate about AI and possessing a learner’s mindset towards LLMs and dynamic systems
Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection) and algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization)
Proven ability to succeed in both collaborative and independent work environments
Demonstrated willingness and track record of engagement with the technical community