TripAdvisor Needham, MA, USA
Oct 11, 2019Internship
TripAdvisor is the largest travel site in the world. We get millions of unique visitors on a monthly basis. To make that possible and to provide the best possible experience to our users, we rely on great employees. Our Data Scientists are massively important in the defining and refining our strategies. We are seeking an exceptional, independent, data-driven individual to join our data science team in the Hotels division for the Summer of 2020. You will be working in a fast-paced environment where you’ll collaborate with a multidisciplinary team of smart people including data scientists, analysts, software engineers, and product managers. You’ll be encouraged to take ownership of your projects and to find new opportunities and problems where machine learning could be applied to improve the business. Responsibilities Perform independent analysis on massive amounts of user data. Develop machine learning models to solve a variety of complex business problems (Personalization, Ranking Algorithms, Recommendations, Search Engine Marketing, Facebook Advertising). Prototype new bidding methods, write specifications, and collaborate with engineering to test and migrate new approaches into production code. Automate ETL and analytics processes. Communicate the results to business stake-holders. Requirements Working towards a Bachelor’s, Master’s, or Doctorate Degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field. A results-oriented person who thrives on data and measurement. Ability to extract actionable recommendations from imperfect data of formidable size. Proficiency with a statistical programming environment (e.g. Python, R). Proficiency in data manipulation, cleansing and interpretation. Close familiarity with SQL and Big Data technologies (e.g. Hive, Spark, etc.) Strong problem solving skills with a pragmatic approach to addressing challenges. Ability to work well with many types of people across multiple global offices. Comfortable with multi-tasking in a very fast-paced work environment.