Data Scientist／AI Technology Business (expired)

【Job Overview】
データサイエンティストとして、弊社事業のAI・機械学習分野での開発をお任せします。
データ分析・可視化、機械学習モデルの開発・導入、最先端のAI/機械学習技術の開発に取り組んでいただきます。

【具体的には】

・プロダクト開発チームと協力し、AI/機械学習機能のニーズや課題の特定・提案・開発
・AI/機械学習機能のPoCとプロトタイプ開発
・AIリサーチャーやデータサイエンティストと協力しての、AIソリューション開発
・プロダクトにおける機械学習モデルの開発・導入
・世界クラスの機械学習およびデータ分析コンペへの参加
・AI/機械学習分野の最先端技術を開発するための研究プロジェクトへの参加


『You will work on a diverse range of problems, including data analysis and visualization, development of ML models, development of cutting edge AI/ML technologies.』

【Responsibilities】

・Collaborate with Business product development teams to find the needs, the problems and propose innovative AI/ML features for products.
・Develop Proof of Concept and prototypes for new AI/ML features
・Collaborate with AI researchers and AI engineers to develop AI solutions for our real world, large scale problems
・Analyze data and develop Machine Learning models for products.
・Participate in world-class machine learning and data analysis competitions
・Participate in research projects to develop cutting edge technologies for AI, Machine Learning fields


【Minimum qualifications】

・MS degree in one of the fields: Computer Science, Mathematics, Operations Research, Computational Biology, Physics, Electrical Engineering, Industrial Engineering or related technical field.
・Programming experience in one or more of the following: C, C++, Java and/or Python.
・Strong background in Machine Learning, Data Mining, Natural Language Understanding or Recommendation or Computer Vision.

【Preferred qualifications】

・Strong interest in Japanese culture,　Japanese-Language Proficiency Test (JLPT) N3 or above
Ph.D. degree in fields as listed in the minimum qualifications.
・Experience working with popular ML and data analysis libraries such as Scikit-learn, Numpy, one of the popular DL frameworks such as Tensorflow, Keras, Pytorch, Chainer
・Relevant work experience, including experience working within the industry or as a researcher in laboratories, especially on topics such as recommendation engines, NLP applications, chatbot.
・Participation in international AI/ML competitions (e.g., Kaggle) and achievement of the top 10% or above in one of those competitions.

・Publishing scientific papers at conferences such as NIPS, ICML, ACL, CVPR, etc.