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KimHak B.
Data Science: end-to-end experienced Skills: Python, ML/DL frameworks, R, Docker, DBMS (OCP)
經歷
Solve complex real-world large-scale end-to-end data science problems; problem framing, data preparation, modeling, development, deployment, management. Use Python, ML/DL frameworks, Docker. Research focuses on advanced analytics of business/consumer data.
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Designed and implemented prediction models using deep learning; supervised, unsupervised approaches. End-to-end ML development. Used Python, TensorFlow, PyTorch, Java.
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Deep Learning image recognition and classification startup. Acquired by a game company. Developed and maintained mobile game servers with DAU of 90k. Used Java, Spring, Redis, MongoDB, MySQL, Docker, Kubernetes, Jenkins, Ansible.
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Full stack development of a Deep Learning machine translation solution company. Developed TMS (Translation Management System), and automation testing tool for machine translation solution. Used Java, Spring, Node.js, and Selenium. Troubleshot and investigated issues to create patches.
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Lead IdM (SSO) implementation project of 40+ target services. Developed modules for various services and technologies.Analyzed, designed, implemented, and delivered. Used Java, DB, LDAP, IdM solution.
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學歷
Bachelor of Science in Industrial Engineering with Finance Certificate. GPA 3.2 / 4.0 (Honors, all terms dean’s list) General manager of student association (2007 – 2009)
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活動
프로젝트
Advanced Clustering
2019년 11월 - 현재 · 6년 3개월
researching and developing advanced multi-aspect customer clustering framework, using consumer transactional data. Patent pending.
프로젝트
Recommender System
2018년 3월 - 2018년 8월 · 6개월
designed and developed recommender system using ensemble of CF (neural network, association rule), unsupervised (clustering), and rule-based models. Delivered model is used on recommendation section of company’s website landing page.
프로젝트
Targeting System
2017년 8월 - 2018년 2월 · 7개월
designed and developed user behavior prediction system using neural network algorithm. Delivered model is used in all digital target marketing activities to maximize RoI. Input data: customer, purchase, product, review, system log, other output: purchasability by product, and customer