Data Scientist II - Performance Marketing

Job Description

Tripadvisor is seeking a Data Scientist II to join their Performance Marketing team in Lisbon, Portugal. The successful candidate will apply their modeling skills to various problem spaces, including bidding for online advertising, customer modeling, experimental design, revenue optimization, attribution, and recommendations. They will collaborate with a multidisciplinary team of data scientists, analysts, software engineers, and product managers.

The Data Scientist II will have the opportunity to work with the newest development tools and huge datasets, deploying solutions online and observing the impact of their work in real time.

What this role involves:

• Using machine learning models to solve core business problems across performance marketing.
• Seeking out new opportunities to apply data science and machine learning in the performance marketing space across all channels (e.g., Web, email, paid marketing).
• Automating ETL pipelines.
• Prototyping, evaluating, deploying, and maintaining new models in production.
• Designing AB tests and analyzing their results.
• Discovering new ways to analyze and interpret the data.
• Communicating progress and interpretation of experimental results to technical and business stakeholders.

Requirements:

• PhD or Masters in Computer Science, Engineering, Statistics, or related field preferred (or masters with 2+ years of practical experience).
• Knowledge of AB test design and analysis.
• Strong background in machine learning and statistics.
• Solid foundation on data structures and algorithms.
• Proficiency in Python for numerical/statistical programming (Numpy/Pandas/Scikit-learn).
• Ability to interpret and write complex SQL queries.
• Experience with big data technologies, such as Hive and Spark.
• Track record of leading the deployment and maintenance of models.

What this role offers:

• Opportunity to work with the newest development tools and huge datasets.
• Chance to deploy solutions online and observe the impact of their work in real time.
• Collaboration with a multidisciplinary team of data scientists, analysts, software engineers, and product managers.
• A culture of personal development, including social activities, journal clubs, memberships in online learning resources, and participation in industry conferences.