Data ScienceMid-career
Data Science × Mid-career
With five years of hands-on experience in data science, I have led the development of recommendation engines and demand forecasting models in the e-commerce and retail sectors. My strengths lie in translating business challenges into mathematical models and applying MLOps to deploy solutions into production. I am adept at creating technical documentation in English and collaborating with global teams, driving data-driven ROI improvements across organizations.
📌 Career Summary
- ▸Apr 2018 - Mar 2021: Data Analyst and Scientist at a major e-commerce platform company, specializing in demand forecasting, marketing ROI analysis, and A/B test design.
- ▸Apr 2021 - Present: Senior Data Scientist at an AI tech startup (foreign-funded), focusing on MLOps for recommendation systems, developing deep learning models, and mentoring junior team members.
📌 Achievements & Strengths
- ▸Developed and deployed demand forecasting models (LightGBM/Prophet) from scratch, reducing inventory waste by 15% year-over-year and achieving annual cost savings of approximately ¥120 million.
- ▸Optimized a personalized recommendation API, reducing inference latency from 50ms to 12ms and increasing click-through rate by 2.4%, contributing to monthly sales of ¥30 million.
- ▸Implemented an in-house MLOps pipeline (Kubeflow/MLflow), dramatically shortening the lead time from model validation to production deployment from three weeks to two days.
📌 Skills
- ▸Languages & Databases: Python (Pandas, NumPy, Scikit-learn), SQL (BigQuery, Redshift), R, Bash
- ▸Machine Learning & Deep Learning: PyTorch, XGBoost, LightGBM, Natural Language Processing (Hugging Face Transformers)
- ▸Infrastructure & MLOps: AWS (SageMaker, S3), Docker, Kubernetes, MLflow, Airflow, dbt
- ▸Business & Other: A/B test design, causal inference, business-level English (TOEIC 890, experience with daily Scrum in global teams)
📌 Self-PR
- ▸I consistently prioritize not just improving model accuracy, but also ensuring business implementation and value creation. In my previous role, I experienced rejection of a high-accuracy deep learning model due to high inference costs, which underscored the importance of a business perspective. Since then, I have collaborated with engineers to build an MLOps foundation, establishing a system for automated deployment of lightweight yet accurate ensemble models. This not only improved the division's data literacy but also earned trust for the data science organization. I am eager to tackle larger and more diverse challenges on a global data platform, which is why I am applying to your company.
💡 Key Tips
- 💡When listing achievements, focus on business impact (e.g., revenue, cost savings) rather than just model metrics (e.g., AUC) to significantly enhance your appeal to business-side stakeholders.
- 💡Highlighting MLOps and data engineering skills is a powerful differentiator in current data science hiring, so be sure to include infrastructure experience.
- 💡If targeting foreign companies, adding URLs to your GitHub portfolio, Kaggle medals, or English-language technical blog in your resume is highly effective.
- 💡In your personal statement, incorporate examples of communication with business teams or stakeholder engagement to demonstrate senior-level perspective and leadership.
🔗 🔗 Other levels
Last reviewed: 2026-09-06 · aijobs Editorial