楽天グループ株式会社

◇ポジション名：Machine Learning Engineer, Data Intelligence Office, Rakuten Travel, Marketing Optimization Project - Travel Development Department (TDD)（1018585）

◇Department Overview
Data Intelligence Office is dedicated to leveraging data science and machine learning to provide solutions that enhance the various functions of Rakuten Travel. From product development to marketing, sales, advertising, and quality control, we use data-driven insights and predictive analytics to drive innovation and deliver measurable results.

▼Why We Hire
We are currently seeking talented ML engineers to join our team and take on the responsibility of optimizing Rakuten Travel's marketing activities. In a rapidly changing online travel industry, it is crucial that we provide our customers with a more personalized and tailored experience. As such, we are constantly on the lookout for enthusiastic data scientists who can help us deliver the best possible value to our customers. Additionally, we offer a supportive environment where we analyze user logs and run various experiments to continuously improve our products through iterative development.

◇Position Details
As a member of our team, you will work closely with business side to understand their challenges, collaborate with data engineers and front-end engineers, and deliver solutions that optimize Rakuten Travel's product offerings. Specifically, you will be responsible for projects such as item recommendation, user targeting, image optimization, and NLP tasks utilizing user reviews. In this role, you will have the following key responsibilities:

・Collaborate with business side to define project requirements.
・Develop detailed dev specifications.
・Build and deploy ML models via APIs.
・Design and execute experiments to validate solutions.
・Conduct AB tests and bandit optimizations to compare different approaches.
・Evaluate experimental results using statistical tests to measure impact and effectiveness. 

◇Mandatory Qualifications
・Master's degree or higher in a quantitative field such as Statistics, Mathematics, Econometrics, Computer Science, Physics, Engineering, Bioinformatics, or a related field or equivalent practical experience.
・At least 3 years of industrial experience building machine learning/deep learning models, including those related to image processing, NLP, and other related fields.
・Strong proficiency in SQL and distributed system optimization, including tools like Spark, Presto, Hadoop, and Hive.
・Fluent in Python and able to effectively use it in data analysis and modeling.
・Demonstrated expertise in experimental design and statistical analysis, including experience with A/B testing, multi-armed bandits, and causal inference from observational data.
・Proven ability to work in a cross-functional team environment and collaborate effectively with other teams.
・Strong desire to stay up-to-date with the latest technologies and trends in the field of machine learning and data science.

◇Desired Qualifications
・Proven experience in deploying machine learning systems to a production environment.
・Fluency in Japanese is considered a plus.
A track record of publications in top international conferences, demonstrating expertise in the field of machine learning and data science. 

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正社員 

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楽天グループ株式会社

Machine Learning Engineer, Data Intelligence Office, Rakuten Travel, Marketing Optimization Project - Travel Development Department (TDD)（1018585） 

289,674百万円 

1997年02月07日 

東京都世田谷区玉川一丁目14番1号 楽天クリムゾンハウス 

◇Why We Hire

We are currently seeking talented ML engineers to join our team and take on the responsibility of optimizing Rakuten Travel's marketing activities. In a rapidly changing online travel industry, it is crucial that we provide our customers with a more personalized and tailored experience. As such, we are constantly on the lookout for enthusiastic data scientists who can help us deliver the best possible value to our customers. Additionally, we offer a supportive environment where we analyze user logs and run various experiments to continuously improve our products through iterative development. 

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