Senior Machine Learning Scientist

TripAdvisor Lisbon, Portugal / Remote

Company

TripAdvisor

Location

Lisbon, Portugal / Remote

Type

Full Time

Job Description

We believe that we are better together, and at Tripadvisor we welcome you for who you are. Our workplace is for everyone, as is our people powered platform. At Tripadvisor, we want you to bring your unique identities, abilities, and experiences, so we can collectively revolutionize travel and together find the good out there.

About the Role

The Tripadvisor Data Science Team is looking for an exceptional experienced individual to help lead the design and building next generation systems to understand our travelers and products to improve customer experience. Such work will allow a significant improvement in our ability to offer to travelers the broadest, most relevant travel products. This individual will have the opportunity to lead projects on the team, including those related to text processing, recommendations, sentiment analysis, topic detection and more.

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Tripadvisor is the web's leading travel information site. At Tripadvisor, the Data Science/Machine Learning Team creates systems that influence the travel decisions of millions of people per day. It applies cutting edge machine learning techniques to a wide variety of areas, including recommenders, vision, information retrieval, and natural language processing. To ensure continued success, Tripadvisor promotes a culture of personal development, including social activities, journal clubs, memberships in online learning resources, and participation in industry conferences. Have a great time while shaping the future of travel!

What You'll Do
  • Seek out new opportunities to apply data science and machine learning to the business (e.g., personalized recommendations, performance marketing, customer modeling, content moderation) across all channels (e.g., Web, native, email, paid marketing, SEO)
  • Collaborate with other teams to solve problems and identify opportunities, managing priorities and deadlines
  • Lead the design of machine learning models to solve a variety of core business problems in customer acquisition, retention, and reactivation
  • Automate ETL pipelines
  • Design, implement, test, deploy, and maintain scalable ML models and systems in production.
  • Communicate progress and interpretation of experimental results to technical and business stakeholders
  • Develop advance techniques in language modeling, transfer learning, in-context learning (zero-shot, few-shot etc.), recommender systems, learn-to-rank models, statistical inference and deep learning
  • Design AB tests and analyze their results
  • Discover new ways to analyze and interpret the data
Skills & Experience
  • PhD or Masters in Computer Science, Engineering, Statistics, or related field preferred
  • Solid foundation of data structures and algorithm
  • Ability to write complex SQL queries
  • Track record of leading the deployment and maintenance of models
  • 3+ years of relevant, practical experience
  • Knowledge of AB test design and analysis
  • Experience with Personalization, recommender systems, collaborative filtering, embeddings, and user lifetime value
  • Experience building data pipelines with tools such as Kubeflow, Argo, Jenkins
  • Experience deploying and maintaining models in cloud environments such as Amazon AWS
  • Experience with model tracking and deployment tools such as MLFlow, Seldon, and Sagemaker
  • Experience following Software Engineering best-practices with Python + Pandas skills
  • Team player
  • Loves to learn
  • Great communication skills with both technical and non-technical partners
  • Gets things done on time and on specification
  • Can work independently
We strive to create an accessible and inclusive experience for all candidates. If you need a reasonable accommodation during the application or the recruiting process, please make sure to reach out to your individual recruiter or our team at [email protected]

#LI-SM1

#LI-Remote

#LI-Hybrid

Apply Now

Date Posted

01/24/2025

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