Data Science Engineer - System Development Group, FinTech System Development Department, Rakuten Card Co., Ltd.

Key Skills

logo, Java Programming, Python Programming, MongoDB, TypeScript, Node.js, SQL, Git, RDBMS, Machine learning techniques, Jenkins, Google BigQuery, Data Visualization, Presto, Apache Hive, Apache Airflow, Docker, Continuous Integration & Continuous Delivery - CI/CD, GraphQL, Grafana, Elasticsearch, MySQL, Algorithms, MLflow, Linux Operating system, SPARK Programming, Streaming APIs, Data pipelines, Luigi, Data Analysis

Job Description

Our FinTech Group Company offers various kinds of financial services such as payment services like our credit card business, point programs at offline stores, internet banking, securities, and insurance.

Department Overview

The FinTech System Development Department improves operations across the FinTech Group Company through system development utilizing FinTech. In this department, you will be able to analyze and provide an optimal system for the whole FinTech business based around various issues and data among the individual businesses.

You will be assigned to a team which promotes data utilization across the FinTech Group Company by providing analysis services, data utilization training programs, developing systems for better data utilization, and supporting the development of an integrated DWH for all FinTech subsidiaries.

Not only will you be able to conduct analyses, but you will also be able to fully utilize data considering systemization and its operations to continuously contribute to the business. Based on your skills and career plan, you will also have the option to work on various other projects from pure software engineering to SRE, MLOps, and data science.

Why We Hire

We have almost completed the phase of spreading our data utilization promotion activities to across the FinTech Group Company; therefore, we are seeking new members who can work together on data analyses and system development to deepen our business contributions and optimize data utilization in all FinTech subsidiaries.

Position Details

Based on your career plan and experiences, you will be assigned to one or more of the following three roles:

Data Analyst / Data Scientist

This role is meant to provide the business value through data analyses and scaling out activities by solving ad-hoc business issues and providing education programs for accelerating data democratization.
You will need to take spontaneous action to achieve the role's mission by offering proper consultation to business members, retrieving data, feature engineering, developing models for each analytical topic, conducting evaluations, etc.

The target of this role consists of ad-hoc analyses for solving business issues for embedding machine learning models in our system. Here, please note that you will need to have native-level proficiency in Japanese to communicate with business members to solve their data issues.

MLOps Engineer

This role is meant to manage our machine learning models and data pipelines, and to develop systems to optimize management work.

You will need to develop systems to continuously provide optimal value to our customers by managing machine learning models appropriately. Concretely speaking, you will be in charge of managing models and data, monitoring data-related metrics, and developing re-training functions.

We gladly welcome non-Japanese engineers, as all engineering work and meetings can be done in English.

Data Engineer / Application Engineer

This role is meant to establish optimal applications and infrastructure to provide maximum value from data utilization continuously to our customers.

You will need to provide business value to our system users and end users by not only satisfying given requirements, but also improving systems proactively individually and cohesively with project managers and team members.

All the above roles require the ability to proactively consider, propose, and implement an application to provide our customers with better business value through our system. You will have many opportunities to closely talk with business members; thus, you will have many chances to realize the value you are producing through your work.

Work Environment

The ratio of people mainly in charge of system development and those of data analysis is about 2 to 1, and you will work together with contractors and members of different teams to drive your projects forward.

Mandatory Qualifications:

Experience:

• 3+ years practical experience in machine learning, data analysis, data visualization, related system development related, or big data processing
• Experience in developing and managing not only ad-hoc scripts for analysis but also web-based applications or batch applications

Skills:

• Ability to do your work such as analysis, development, and system management, not only from an engineering point of view but also from a business point of view (You will need to have skills to implement services while considering them from a technical point of view and from a broad strategic point of view to contribute to our business)
• Ability to investigate, select, learn, and utilize the best technical stacks like programing languages, middleware, databases, etc. to solve issues.

Desired Qualifications:

Those who satisfy the following conditions are highly welcome, and we especially welcome those who have experience in multiple areas. If necessary, you may have to learn new technologies as well.

Common

• Experience of development or analysis in a specific area (financial area is highly welcome), and have domain knowledge on the field
• Experience of leading developers or analysts in a Tech Lead position
• Capability of writing efficient queries based on the knowledge on a typical database and its algorithm
• Experience of designing, developing, and managing RDBMS like MySQL, and tuning its performance
• Experience of designing, developing, and managing non-RDBMS like Redis, MongoDB, and Cassandra, and tuning its performance
• Basic knowledge on Linux usage
• Basic knowledge on Git usage
• Experiences of Scrum development

Data Analytics Specific

• Good understanding of typical machine learning algorithms
• Good understanding of typical evaluation methods for machine learning models
• Good understanding of typical methods to interpret machine learning models
• Experience in education and promotion of data utilization for data democratization
• Have practical experience with AutoML

MLOps Specific

• Experience of designing, developing, and managing an ML lifecycle management system using tools like MLFlow
• Experience of designing, developing, and managing a data management system using tools like DVC
• Experience of designing, developing, and managing parallel distributed processing systems running on the Hadoop ecosystem like Apache Spark
• Experience of designing, developing, and managing data pipelines using some workflow engines like Apache Airflow, Luigi, Digdag, etc.
• Experience of designing, developing, and managing systems using distributed SQL engines like Presto, Apache Hive, BigQuery, etc.

Application Engineering Specific

• Experience of designing, developing, and managing backend APIs especially using Go, Java, Node.js, Python, etc.
• Experience of library version control using Go module, Maven, npm, pip, etc.
• Experience of designing, developing, and managing frontend systems using React, Vue, Angular, etc., especially utilizing Typescript type system.
• Experience of developing BFF using GraphQL
• Experience of system development utilizing containerization technologies like Docker and Kubernetes
• Experience of system development utilizing full-text search engines like Elasticsearch
• Experience of building up CI/CD pipelines using Jenkins
• Experience of designing, developing, and managing distributed systems like microservices architecture
• Experience of designing, developing and managing log collection and analysis platforms using the ELK stack
• Experiences of designing, developing, and managing metrics collection and analysis platforms like Datadog, Prometheus, Grafana, etc.