ML Modeling Engineer

■ Job Description You will be mainly responsible for high-priority tasks currently being promoted by the team, with the aim of reducing the workload of implementing algorithms into major products and company-wide AI utilization. We anticipate that you will be given priority in areas of expertise and areas of interest. Specifically, the following tasks are expected: [ML Modeling Engineer (Data Scientist) Responsibilities] * Further enhancement of existing product matching algorithms: Mutual recommendation models, recommendation engines, etc. * AI agent development and promotion of company-wide AI implementation: Development work aimed at reducing workload associated with company-wide AI utilization; Development of internal business efficiency tools utilizing generation AI APIs and Slack Bots; Visualization using BI dashboards; Data quality management updates, etc. ■ Recruitment Background: We are recruiting ML engineers due to business expansion into a new domain called the "Life Hub Concept," the need to enhance our existing core business matching algorithms, and the increasing need to reduce company-wide workload through AI utilization. ■ Attractiveness of this Position: You can create value for both the product and the company; you have a high degree of autonomy and can move quickly in a problem-driven manner; you can be involved in all phases of AI utilization, from PoC to actual operation; you can promote AI development while communicating closely with both the business and development sides; you can directly contribute to major life events for users. ▼Required Conditions (All are required) ・Basic Python/SQL skills ・Experience implementing ML/optimization code in research, internships, or personal projects (OSS, Kaggle, AtCoder-related communities, etc.) ・Understanding of fundamental machine learning theory ・Minimum knowledge of linear algebra and calculus, and the ability to understand/explain the overview. ▼Preferred Conditions ・Knowledge and practical experience of recommendation engines, mutual recommendation, collaborative filtering, Learning-to-Rank, re-ranking, etc. ・Experience in A/B test design, causal inference, and effectiveness verification using propensity score matching ・Experience operating models using MLOps tools (SageMaker, CI/CD, etc.) ・Experience using cloud infrastructure (AWS/GCP) (especially SageMaker, BigQuery, S3) ・Experience developing with coding agents (Codex/Claude code, etc.) ■ Languages ・Japanese Level: Business Level (JLPT Level 2 or N2)