Data Scientist (Guest Travel Insurance, Algorithms)

Airbnb
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
$ 210K

Job location

Remote

Tech stack

A/B testing
Artificial Intelligence
Computer Vision
Machine Learning
Recommender Systems
Working Model 2D
Reinforcement Learning
Feature Engineering
Large Language Models
Deep Learning
Machine Learning Operations
Data Pipelines

Job description

  • We're looking for a machine learning expert who is excited to own hard problems end-to-end-from prototype to production. You'll have direct scope to contribute and lead across:
  • Package personalization & ML-based recommendation: Evolve rule-based guest segmentation into a full ML recommendation system that surfaces the right insurance (e.g., trip cancellation, accidental damage coverage, on-trip protection) to each guest based on purchase intent, trip attributes, listing signals, and user history
  • Content personalization: Build models that rank and select benefit messaging for each guest-deciding which coverages to highlight, in what order, and with what framing-drawing on learnings from segmentation experiments and LLM-assisted content prototyping
  • Intent modeling: Develop and productionize ML models (from gradient-boosted trees to deep learning) that predict a guest's likelihood to value specific coverages, using structured booking data and unstructured signals
  • Journey understanding and optimization: Leverage reinforcement learning to personalize across user journey, with understanding on user preferences on entry point, price, notification frequency, and trip characteristics
  • High-velocity experimentation: Design and run adaptive experiments to maximize learning within tight traffic constraints; sequence ERFs strategically to keep the personalization roadmap moving
  • A Typical Day:
  • Dig into experiment results to surface high-impact personalization opportunities; translate what you find into crisp scientific problem formulations that balance rigor with speed-to-learning
  • Work closely with product managers, engineers, operations, legal, and privacy partners to align on ML requirements, de-risk design decisions, and gather requirements on explainability and compliance
  • Hands-on develop, evaluate, and ship ML models and data pipelines at scale-batch and real-time, structured and unstructured-using Airbnb's paved-path tooling and AI native mindset
  • Prototype and iterate quickly: turn a new idea into a working model in a prototype, get early signals from an experiment, then productionize what works. You move fast and don't wait to be asked
  • Present findings and proposals at team reviews and to technical, product, and executive stakeholders-making complex ML results legible without dumbing them down, and generating conviction on the roadmap ahead
  • Stay current with the research community; draw on state-of-the-art advances in recommendation systems, LLMs, and personalization to raise the bar for what the team ships. Occasionally publish externally or present at conferences to advance Airbnb's scientific standing

Requirements

  • Strong fluency in Python and SQL; hands-on experience with TensorFlow or PyTorch, Airflow, and a data warehouse environment
  • 5+ years of relevant industry experience (e.g., ML scientist, tech lead, junior faculty) and a Master's degree or PhD with 2+ yrs in a relevant field
  • Deep understanding of ML algorithms (gradient-boosted trees, deep learning, optimization) and experiment design-including A/B testing, multi-armed bandits, and the practical constraints of running experiments at scale. Causal inference skills are a plus
  • Product-oriented mindset: you keep the guest experience at the center of technical decisions and bring conceptual and innovative thinking to how you frame and solve problems. Publications or presentations in recognized venues are a plus
  • Proven hands-on experience building and shipping personalization and recommendation systems at scale: strong intuition for feature engineering, user modeling, and the full ML lifecycle (training, serving, monitoring, iteration). Experience with LLMs, Computer Vision or content-understanding topics is a strong plus
  • Self-directed and passionate: you're energized by a fast-moving environment where there are always more good ideas than time; you hold yourself to a high standard without being asked, take initiative to unblock yourself, and find genuine satisfaction in shipping things that matter to guests
  • Exceptional communicator: you can make complex ML work legible to engineers, product managers, legal, and executives alike- written and verbal. You treat communication as a core part of the job, not an afterthought

Benefits & conditions

  • Paid volunteer time
  • Health food and snacks
  • Generous parental and family leave
  • Learning and development
  • Annual travel and experiences credit

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