Role Overview
Airbnb is seeking a Data Scientist specializing in Machine Learning and Personalization to join the AirCover team. This role focuses on enhancing guest safety and peace of mind through intelligent systems for Guest Travel Insurance (GTI). You will work at the intersection of insurance, personalization, and machine learning, building systems that deliver the right coverage to the right guest at the right moment. This is a high-output team with an experiment-dense personalization roadmap, partnering closely with product, engineering, operations, and legal.
The Difference You Will Make
We are looking for a machine learning expert passionate about owning complex problems end-to-end, from prototype to production. Your scope will include contributing to and leading efforts across:
- Package personalization & ML-based recommendation: Evolve rule-based guest segmentation into a full ML recommendation system to surface relevant insurance (e.g., trip cancellation, accidental damage coverage, on-trip protection) based on purchase intent, trip attributes, listing signals, and user history.
- Content personalization: Build models to rank and select benefit messaging for each guest, deciding which coverages to highlight, their order, and framing, drawing on segmentation experiments and LLM-assisted content prototyping.
- Intent modeling: Develop and productionize ML models (from gradient-boosted trees to deep learning) to predict a guest's likelihood to value specific coverages, utilizing structured booking data and unstructured signals.
- Journey understanding and optimization: Leverage reinforcement learning to personalize across the user journey, understanding 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, strategically sequencing ERFs to advance the personalization roadmap.
A Typical Day
- Analyze experiment results to identify high-impact personalization opportunities, translating findings into precise scientific problem formulations that balance rigor with learning speed.
- Collaborate with product managers, engineers, operations, legal, and privacy partners to align on ML requirements, de-risk design decisions, and gather requirements for explainability and compliance.
- Hands-on develop, evaluate, and deploy ML models and data pipelines at scale (batch and real-time, structured and unstructured) using Airbnb's established tooling and AI native approach.
- Prototype and iterate quickly: transform new ideas into working models, gain early signals from experiments, and then productionize successful solutions. You will be proactive and self-driven.
- Present findings and proposals at team reviews and to technical, product, and executive stakeholders, making complex ML results clear and generating conviction for future roadmap directions.
- Stay updated with research advancements; apply state-of-the-art developments in recommendation systems, LLMs, and personalization to elevate team deliverables. Opportunities to publish externally or present at conferences may arise to enhance Airbnb’s scientific standing.
Your Expertise
- 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 deploying 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: capable of making complex ML work legible to engineers, product managers, legal, and executives alike—both written and verbal. Communication is treated as a core part of the job.
- Self-directed and passionate: thrives in a fast-moving environment with numerous good ideas; maintains high standards without prompting, takes initiative to unblock oneself, and finds satisfaction in delivering work that significantly impacts guests.
- Product-oriented mindset: prioritizes the guest experience in technical decisions and brings conceptual and innovative thinking to problem framing and solving. Publications or presentations in recognized venues are a plus.