Data ScienceSenior
Data Science × Senior
With over 10 years of experience in data science, I have led machine learning model design and MLOps implementation in the finance and e-commerce sectors. I specialize in developing recommendation engines that contributed to annual revenue increases of several hundred million yen and building data infrastructure for products with over 1 million DAU. My strength lies in driving projects end-to-end, from identifying business challenges and conducting PoCs to deploying in production and managing teams.
📌 Career Summary
- ▸Worked as a consultant at a major SIer, focusing on data warehouse construction and BI implementation. Responsible for requirements definition and data modeling (3 years).
- ▸Moved to a foreign-affiliated tech company, where as a senior data scientist, I led the development of e-commerce recommendation algorithms and standardized MLOps pipelines (5 years).
- ▸Currently serving as a manager of a data science team (8 members) at a major domestic financial institution's DX promotion office, working on AI-driven credit models (2 years).
📌 Achievements & Strengths
- ▸Revamped the personalized recommendation model for an e-commerce site, increasing CVR by 14% and contributing to an annual revenue increase of 2.5 billion yen.
- ▸Introduced an AI-based credit screening model, reducing average screening lead time from 3 days to 15 minutes and decreasing the default rate by 0.8%.
- ▸Developed an in-house MLOps platform (Kubeflow, MLflow), shortening the lead time for model deployment from 4 weeks to 3 days.
📌 Skills
- ▸Languages and Frameworks: Python, SQL, PyTorch, TensorFlow, Scikit-learn, Spark
- ▸MLOps and Infrastructure: Docker, Kubernetes, Kubeflow, MLflow, AWS (SageMaker, Redshift, Glue), GCP (BigQuery, Vertex AI)
- ▸Data Engineering: dbt, Airflow, Kafka
- ▸Business and Management: Project management, team building, stakeholder negotiation, English (business level / TOEIC 920)
📌 Self-PR
- ▸I am a data scientist who prioritizes 'business impact' over 'technology for its own sake.' At my previous e-commerce company, I proposed a cutting-edge deep learning model, but the business side raised concerns about inference cost and latency. In response, I devised and implemented a hybrid model that ensembles lightweight XGBoost and a neural network. This approach reduced inference costs by 60% while maintaining accuracy, ultimately driving an annual revenue increase of 2.5 billion yen. This experience has solidified my belief that my greatest strength lies in balancing technical curiosity with business constraints and guiding stakeholders toward an optimal solution.
💡 Key Tips
- 💡The definition of 'management' differs between foreign-affiliated and domestic Japanese companies. Tailor your resume to emphasize 'coordination and consensus-building' for Japanese firms, and 'technical leadership and metric-driven team building' for foreign firms.
- 💡At the senior level, it's essential to highlight not just 'hands-on technical work' but also 'how you identified business problems and maximized ROI.' Prepare to discuss a PoC project that didn't go into production and what you learned from it; this will boost your evaluation in interviews.
- 💡Experience in MLOps and data governance is highly valued in the current market, so actively showcase numbers that demonstrate how you reduced operational and maintenance costs, not just model development.
🔗 🔗 Other levels
Last reviewed: 2026-09-06 · aijobs Editorial