Data Scientist, Machine Learning (Guest Travel Insurance)

Role Overview

Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. As a Data Scientist on AirCover, you will work at the intersection of insurance, personalization, and machine learning, building intelligent systems that help the right guest discover the right coverage at the right moment. You'll join a tight-knit, high-output Data Science team that runs one of Airbnb's most experiment-dense personalization roadmaps, partnering daily with product, engineering, operations, and legal to ship work that directly affects guest trust and revenue.

Responsibilities

We are looking for a machine learning expert excited to own hard problems end-to-end, from prototype to production. You will 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 the user journey, with understanding of 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 will include:

• Digging into experiment results to surface high-impact personalization opportunities; translating findings into crisp scientific problem formulations that balance rigor with speed-to-learning.
• Working 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 developing, evaluating, and shipping ML models and data pipelines at scale — batch and real-time, structured and unstructured — using Airbnb’s paved-path tooling and AI native mindset.
• Prototyping and iterating quickly: turning a new idea into a working model in a prototype, getting early signals from an experiment, then productionizing what works.
• Presenting findings and proposals at team reviews and to technical, product, and executive stakeholders, making complex ML results legible without oversimplifying them, and generating conviction on the roadmap ahead.
• Staying current with the research community; drawing on state-of-the-art advances in recommendation systems, LLMs, and personalization to raise the bar for what the team ships. Occasionally publishing externally or presenting at conferences to advance Airbnb’s scientific standing.

Requirements

• 5+ years of relevant industry experience (e.g., ML scientist, tech lead, junior faculty) and a Master’s degree or PhD with 2+ years in a relevant field.
• 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.
• Strong fluency in Python and SQL; hands-on experience with TensorFlow or PyTorch, Airflow, and a data warehouse environment.
• 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.
• Exceptional communicator: able to make complex ML work legible to engineers, product managers, legal, and executives alike — written and verbal.
• Self-directed and passionate: energized by a fast-moving environment where there are always more good ideas than time; holds oneself to a high standard without being asked, takes initiative to unblock oneself, and finds genuine satisfaction in shipping things that matter to guests.
• Product-oriented mindset: keeps the guest experience at the center of technical decisions and brings conceptual and innovative thinking to how problems are framed and solved. Publications or presentations in recognized venues are a plus.