Senior Data Scientist - Analytics, Guest Experience

San Francisco, CA

Applications have closed

Airbnb

Get an Airbnb for every kind of trip → 7 million vacation rentals → 2 million Guest Favorites → 220+ countries and regions worldwide

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Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. It takes a unified team committed to our core values to achieve this goal. Airbnb's various functions embody the company's innovative spirit and our fast-moving team is committed to leading as a 21st century company.

Airbnb is one of the world’s largest marketplaces for unique, authentic places to stay and things to do, offering over 7 million accommodations and 40,000 handcrafted activities, all powered by local hosts. An economic empowerment engine, Airbnb has helped millions of hospitality entrepreneurs monetize their spaces and their passions while keeping the financial benefits of tourism in their own communities. With more than half a billion guest arrivals to date, and accessible in 62 languages across 220 countries and regions, Airbnb promotes people-to-people connection, community and trust around the world.

About the Team

We are looking for a talented Senior Data Scientist to join the Guest Experience team. The team is responsible for the end to end guest experience on Airbnb, from the landing pages such as the homepage, the search and listing pages, the signup and booking flow and the actual pre and on-trip experience. The Data Science team works closely with product and engineering to power the guest side data ecosystem, influence product development, and enable easy insights and reliable measurement.

Areas of Responsibilities

You will join the Guest Conversion data science team responsible for understanding the guest journey from visiting Airbnb to making a booking. You will work closely with product teams, cross-functional partners, and other data scientists to drive strategy and product changes through insights. In this role you will:  

  • Build a deep understanding of how guests use the product, including where they run into problems
  • Surface strategic guest and travel trends from our data, including how use cases and preferences are evolving
  • Partner cross-functionally to build an easy booking experience and fun planning product for our guest community
  • Develop analytics frameworks and foundations to enable easy actionable insights and reliable measurement.  
  • Collaborate with data engineers on infrastructure, tools and processes that enable better data logging, and manage the timeliness and accessibility of data tables.
  • Think strategically about how to scale the work that you and the teams do. Develop tools and automated processes that project the work out to a broader audience. Strategize on democratizing data and insights to make analyses easily repeatable and generalizable by other team members in the future.

Required Qualifications

  • 5+ years of relevant industry experience
  • Demonstrated track record of product leadership and ability to lead and influence teams
  • Excellent written and oral communication skills
  • Strong skills in SQL and distributed system optimization (e.g. Spark, Presto, Hadoop, Hive)
  • Passion for high data quality and scaling data science work
  • Expert in at least one programming language for data analysis (e.g. Python, R)
  • Familiarity with experimentation
  • Ability to perform basic statistical analysis to inform business decisions
  • Proven ability to succeed in both collaborative and independent work environments
  • Detail oriented and excited to learn new skills and tools

Preferred Qualifications:

  • Product analytics experience with a two-sided marketplace or e-commerce business
  • Experience in schema design and dimensional data modeling
  • Experience with technical mentorship of other data scientists
  • Experience with an ETL framework like Airflow

Tags: Airflow Data analysis E-commerce Engineering ETL Hadoop Python R Spark SQL

Region: North America
Country: United States
Job stats:  2  0  0

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