会社名

楽天グループ株式会社

会社概要

未来を信じ、より良い明日を創っていく。
イノベーションを通じて、人々と社会をエンパワーメントする。私たちは、そんな想いを大切に世界の人々に喜びと楽しさを届けます。
楽天は、E コマース、FinTech、デジタルコンテンツ、通信など、70 を超えるサービスを展開し、世界10 億以上のユーザーに利用されています。
これら様々なサービスを、楽天会員を中心としたメンバーシップを軸に有機的に結び付け、他にはない独自の「楽天エコシステム」を形成しています。ダイバーシティ推進は、楽天にとって最優先の企業戦略のひとつです。従業員の出身は70カ国・地域以上。世界中からユニークで多様な文化的背景や視点を持つ優秀な人材が集まり、イノベーションの原動力になっています。社内カフェテリアにはベジタリアン、ハラル対応のメニューを用意。礼拝所（Prayer room）もあります。
また、仕事と育児の両立支援や、障がい者雇用・活躍促進も積極的に推進。社内のLGBT（※1）当事者やアライ（※2）に対して、情報共有やサポート体制の強化も進めています。誰もが自分らしく力を最大限発揮して働ける。それが楽天のダイバーシティです。

70を超えるサービスを提供し、世界30カ国にサービス展開拠点を持ち、従業員の出身国・地域数は100を超え、オープンポジション制度を活用して多様なキャリアを描くことができる点も魅力です。
フレックスタイム制度、事情に応じたリモートワークの活用が可能です。本社には託児所やフィットネスジム、三食無料で利用可能なカフェテリアが併設されるなど、社員を支える環境が整備されています。

ポジション

1027139　Data Science Engineer - Search Department (SED)

仕事内容

Job Description:
Business Overview
The AI & Data Division (AIDD) creates powerful AI, customer-focused search, recommendation, data science, advertising, marketing, price and inventory optimization solutions to a variety of businesses in commerce, fintech and mobile industries. We design, develop, and deploy high performance, fault-tolerant distributed systems used by millions of Rakuten customers every day. We strive to deliver the most innovative solutions that are helpful to people and societies around the world.

Rakuten is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At Rakuten, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

Position:
Why We Hire
As an experienced Data Scientist, you will need to have a strong background in machine learning, NLP, Computer Vision, Python programming, and data analytics. Our team is responsible for developing state-of-the-art machine learning models and strategies to improve E-commerce conversion through search and recommendation algorithm improvements. You are able to work independently, and have the ability to propose solutions that the team can use, and that can impact projects.

Position Details
The search science team is responsible for developing cutting-edge deep learning models and algorithms solutions for various NLP and multimodal tasks using neural networks, machine learning algorithms, and optimization techniques. The team works on challenging problems such as semantic search, image and multimodal search, deep ranking systems, and more. The team also leverages state-of-the-art frameworks such as transformers and large language model (LLM) to train and deploy efficient and accurate models.
 
Responsibilities
- Build industry-leading search & recommendation system to solve business challenges.
- Develop highly scalable embedding-based retrieval systems and tools.
- Be responsible for improving search recall and ranking by leveraging NLP and multimodal large models to improve multilingual, multi-task, and multi-modal algorithm performance.
- Conduct AB Tests to ensure we pick the best winning variant.
- Ensure successful deployment of features, code, data, and models in production.
- Understand the diversity of Rakuten's business requirements, data, and assets; build cross-scenario technology backbones.
- Work with Rakuten cross-functional engineers, researchers, and statisticians to grow Rakuten in important regional markets.

求める経験・スキル

Mandatory Qualifications:
- 2 year or more experience in one or more of the following areas: Machine Learning, Search, Recommendation Systems, Large Language Models, or other related areas.
- Bachelor's in computer science or a related technical discipline, Master or PhD preferred.
- Solid experience with data structures or algorithms.
- Strong software development experience through hands-on coding in a general-purpose programming language.
- Strong communication and teamwork skills.
- Passion for technology and solving challenging problems.