Senior Data Scientist, Guest Travel Insurance (algorithms)

Airbnb Airbnb · Consumer · United States · Data Science

Senior Data Scientist role focused on building and shipping ML models for guest travel insurance personalization and recommendations. The role involves end-to-end ownership from prototype to production, including intent modeling, content personalization, and journey optimization using techniques like reinforcement learning and LLMs. It emphasizes high-velocity experimentation and collaboration with product, engineering, and legal teams.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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
  5. High-velocity experimentation: Design and run adaptive experiments to maximize learning within tight traffic constraints; sequence ERFs strategically to keep the personalization roadmap moving.

Skills

Required

  • Python
  • SQL
  • TensorFlow or PyTorch
  • Airflow
  • data warehouse environment
  • ML algorithms (gradient-boosted trees, deep learning, optimization)
  • experiment design (A/B testing, multi-armed bandits)
  • communication skills
  • product-oriented mindset

Nice to have

  • LLMs
  • Computer Vision
  • content-understanding topics
  • Causal inference skills
  • Publications or presentations in recognized venues

What the JD emphasized

  • own hard problems end-to-end
  • prototype to production
  • full ML lifecycle
  • shipping personalization and recommendation systems at scale
  • productionize ML models
  • shipping work that directly affects guest trust and revenue
  • ship ML models and data pipelines at scale
  • productionize what works
  • ship things that matter to guests

Other signals

  • personalization
  • recommendation systems
  • intent modeling
  • reinforcement learning
  • LLMs