Job Description:

Business Overview 
Rakuten group has almost 100 million customers in Japan and 1 billion customers around the world, and provides more than 70 services such as e-commerce, payment services, financial services, mobile, media, sports, etc.

Department Overview 
AI Services Supervisory Department (AISSD) provides data-oriented solutions by leveraging data science and Rakuten group’s data gathered from 70+ services. The department contributes to Rakuten’s business units and Rakuten’s business partners as well. We have the strategic vision “Rakuten as a data-driven membership company”. AISSD has the mission to realize it.

Among the AISSD, the Data Science Consulting Department (DSCD) serves as a bridge between the business units and the development units. We propose data-driven solutions based on a deep understanding of the business and swiftly drives their implementation.

Business domain
Commerce & Marketing:
- Driving data solution of Commerce Company of Rakuten Group (Rakuten Ichiba, Rakuma, Rakuten Fashion, etc.) by maximizing value of data.
- Providing data-oriented solutions utilizing clients and Rakuten data for external customers, such as manufacturers, retailers, and local governments, through marketing actions such as ads and media.

Mandatory Qualifications: 

- Computer science or related background 

- 5+ years’ experience in software development, especially using Python as a programming language 

- Experience with common Linux commands and Linux scripting languages 

- Experience with Hadoop, MapReduce, HDFS and Big Data querying tools, such as Tez, Hive, and Impala 

- Experience with designing and building tools based on specific requirements 

- Experience with building and maintaining data science platform 

- Experience with SQL and some of the modern relational databases 　 

Desired Qualifications: 

- Experiences in web service development in multiple industries. 

- Solid knowledge of large volumes data processing 

- Experience with Big Data ML toolkits, such as Mahout, SparkML, or H2O 

- Experience with NoSQL databases, such as HBase, Redis, CouchBase 

- Familiar with data mining concepts and machine learning algorithms 

- Experience with Spark and stream-processing systems, using solutions such as Storm or Spark-Streaming 

- Knowledge of various ETL techniques and frameworks, such as Flume

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