Data Science, Algorithms - Customer Support

Airbnb

3h•Remote

About The Position

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. Customer Support (CS) aims to build the world’s most loyal travel community through exceptional service. Personalizing our CS offerings will allow us to meet our customers when and how they need us most. As a Data Scientist working on Algorithms in CS, you will have the opportunity to collaborate with a strong team of engineers, product managers, designers and operation agents to build scalable and robust systems to match our services to specific customer needs. You will be able to create meaningful impact through deep scientific understanding and by designing interventions for Airbnb CS’s most critical challenges. The Difference You Will Make: For this role, we’re seeking a candidate with ML/LLM proficiency to join the Customer Support Data Science team. You will work closely with the tech lead for this area on significant components of larger projects and have a direct opportunity to contribute and influence in the differentiated service space by designing scalable scientific solutions for problems like: Build ML/LLM models to understand customer issues and identify the most important areas to prioritize for resolutions. Leverage generative AI methods to understand customer service experiences based on diverse datasets, and identify failure modes and opportunities for improvement. Implement advanced techniques to automate the evaluation of LLM, ensuring high efficiency and quality. A Typical Day: Identify high-impact business opportunities through data exploration and prototyping, translating business problems into scientific formulations. Collaborate closely with cross-functional partners—including software engineers, product managers, operations, and analytics—to refine ML/LLM requirements, drive scientific decisions, and quantify business impact. Develop, productionize, and operate models and pipelines at scale for both batch and real-time use cases, structured and unstructured data. Build scalable performance measurement solutions for LLM evaluation using internal tooling, integrating industry best practices and state-of-the-art innovations. Use AI to automate repetitive data science workflows and enhance efficiency.

Requirements

2+ years of relevant industry experience (e.g. ML scientist, tech lead, junior faculty) and a Master’s degree or PhD in relevant fields.
Strong fluency in Python and SQL, experience with Tensorflow, PyTorch, Airflow and data warehouse.
Deep understanding of machine learning lifecycle best practices (e.g. training/serving, feature engineering, feature/model selection, labeling, A/B test), algorithms (e.g. gradient boosted trees, neural networks/deep learning, optimization) and domains (e.g. natural language processing, personalization and recommendation).
Proficiency with LLMs and/or related AI, NLP. For example, BERT, GPT-2/3/4, LLaMA, Mistral.
Proven ability to communicate clearly and effectively to audiences of varying technical levels, observation causal inference skill is a plus.
Ability to take a product-oriented mindset in using conceptual and innovative thinking to develop and apply solutions taking into consideration the user experience.

Nice To Haves

Proven mix of strong intellectual curiosity with high level of pragmatism and engagement with the technical community. Publications or presentations in recognized journals/conferences is a plus.

Responsibilities

Identify high-impact business opportunities through data exploration and prototyping, translating business problems into scientific formulations.
Collaborate closely with cross-functional partners—including software engineers, product managers, operations, and analytics—to refine ML/LLM requirements, drive scientific decisions, and quantify business impact.
Develop, productionize, and operate models and pipelines at scale for both batch and real-time use cases, structured and unstructured data.
Build scalable performance measurement solutions for LLM evaluation using internal tooling, integrating industry best practices and state-of-the-art innovations.
Use AI to automate repetitive data science workflows and enhance efficiency.