Senior Data Scientist, Airbnb
Platform, Integrity
$177-208k
More information about location
Community marketplace for holiday accomodation
Be an early applicant
Community marketplace for holiday accomodation
1001+ employees
Be an early applicant
$177-208k
More information about location
1001+ employees
Company mission
To create a world where anyone can belong anywhere.
Role
Who you are
- 5+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (computer science, statistics etc.), or 2+ years of experience with a Ph.D
- State-of-the-art knowledge of AI/ML models
- Hands-on experience building, evaluating, and deploying NLP and LLM-based solutions, including text classification, information extraction, semantic understanding or generative applications
- Working knowledge of causal inference
- Skilled in statistical programming (Python or R) and database usage (SQL)
- Proven ability to communicate clearly and effectively to audiences of varying technical levels
- Ability to translate complex findings and results into compelling narratives that drive impact
- Excellent project management, communication, and collaboration skills
- Trust & Safety experience is a plus
What the job involves
- This role is unique in that it directly improves the safety, trust, and quality of real-world user experiences by advancing Airbnb’s ability to understand, interpret and act on content at scale
- You will help shape how the platform reasons about listings, profiles, messages and other user-generated content by building the next generation of Trust Content Understanding Models
- The ideal candidate is a motivated and talented “full-stack” Data Scientist with strong applied ML intuition and a bias toward impact, who can own and drive forward challenging, high-visibility initiatives such as:
- Advance Airbnb’s content integrity capabilities by building Natural Language Processing (NLP) and LLM-based models that understand intent, policy compliance, quality and risk across listings, profiles, and user communications
- Develop high-performing models for detecting problematic or misleading content, including text classification, semantic similarity, information extraction and generative model-based reasoning for policy interpretation and enforcement
- Design and optimize human-in-the-loop Machine Learning (ML) systems for content review, labeling, escalation and continuous model improvement
- Build systems to detect emerging content risks and abuse patterns across regions, cohorts and surfaces using statistical, ML and representation-learning approaches
- Design intelligent sampling and evaluation strategies to measure rare events, policy recall, false positives/negatives and model blind spots in large-scale content systems
- Artificial Intelligence / Machine Learning: Build and deploy production AI/ML systems for content integrity and trust content understanding, including feature engineering, model development and evaluation, thresholding, error analysis and end-to-end model lifecycle management
- This includes working with NLP and LLM-based models in real production settings
- Inference: Partner with inference data scientists to conduct rigorous quantitative analyses, applying working knowledge of causal inference to interpret results, assess impact, and identify gaps and opportunities to improve content quality and trust outcomes
- Optimization: Develop frameworks to analyze tradeoffs between enforcement accuracy, user experience, operational cost and coverage, and propose strategies to optimize overall system effectiveness
- Communication & Collaboration: Deliver robust research reports with effective data visualizations, clear storytelling and bullet-proof accuracy to drive forward impact in collaboration with cross-functional partners in product, engineering and operations
- Empowerment: Think strategically about how to scale and evolve Airbnb’s content integrity defenses, helping define the long-term vision for the role of AI-driven content understanding across the Trust ecosystem
Our take
When Airbnb was originally founded, the product helped customers book air mattresses on people's apartment floors. Since then, Airbnb has had over a billion customers hosted through its platform. With more than 4 million registered hosts worldwide, it has grown into a major player in the hospitality sector.
Airbnb has been successful in disrupting the traditional holiday lettings sector. Before it existed accommodation was limited to hotels and bed and breakfasts, but Airbnb has mainstreamed the idea of renting out spare rooms or even entire homes. It has created a new sector of hosts purchasing properties to rent out on the platform and provides a level of flexibility and a range of offerings not previously possible for travelers.
Perhaps a victim of its own success, by 2022 supply of Airbnb-listed properties began to outstrip demand in some markets, as hosts overestimated the appetite for domestic travel. However, the company is still posting strong financials and has unveiled new tools to support its hosts in a tougher rental market. With a healthy bottom line and a huge user base, Airbnb is very likely to overcome the current market turbulence and continue its growth.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
25% employee growth in 12 months
Company
Funding (last 2 of 10 rounds)
Apr 2020
$500m
GROWTH EQUITY VC
Mar 2017
$447.8m
SERIES F
Total funding: $3.8bn
Company benefits
- Paid volunteer time
- Health food and snacks
- Generous parental and family leave
- Learning and development
- Annual travel and experiences credit
Company HQ
Showplace Square, San Francisco, CA
Leadership
Brian Chesky
(Co-Founder & CEO)Previously an Industrial Designer, studying at Rhode Island School of Design.
Nathan Blecharczyk
(Co-Founder & CSO)Studied Computer Science at Harvard, and had around 4 years of professional Software Engineering experience before founding Airbnb, including interning at Microsoft.
Joe Gebbia
(Co-Founder)Also studied Industrial Design at Rhode Island, and previously founded various design-related startups
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