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1.資料前處理: 數據清洗、特徵工程、特徵選取 2.應用數據挖掘、機器學習、深度學習、最佳化等技術，建立推薦與決策模型 3.成效指標設計，模型驗證與優化 4.模型生命週期管理，保持預測效

1. 規劃、執行數據分析或建置預測模型，有效識別商機、客戶風險或行為屬性，以協助達成商業/風險決策或效率 2. 透過顧客交易行為及非結構化資料路徑分析、斷點等分析方法，優化服務體

Rosetta.ai helps fashion e-commerce uncover consumer's shopping preferences and provide personalized shopping experiences to consumers. We're making e-commerce merchants have enterprise-level AI backup on day 1. We are seeking Deep Learning Engineers, dedicated to Recommender System, to join our data science team. The ideal person will have industry experience working on a range of classification and optimization problems, e.g.click-through rate prediction, recommendation systems, or search

1. 與客戶溝通進行需求訪談 2.企業資料倉儲需求分析及文件撰寫 3.依專案整合串接不同系統資料,各種數據轉換管理,以利資料庫正常運作 4.使用者行為數據收集,加工.分析，以支

工作內容 1.規劃並開發AI、Big data模型。 2.智慧製造、AI、AIoT、Big data、邊緣系統相關AI軟體開發及運維。 3.配合部門主管工作調度、分配。

【工作內容】 【工作內容】 1. 未來預測演算法開發。(處理時間x空間之相關數據資料) 2. 持續優化並修正AI gen AI自我學習演算系統流程與設計。 3. 數據分析、應用之相關程式開發。 4. API、爬蟲數

At Pickupp, we heavily rely on data to make decisions. Indeed we believe some of the hard problems here in the technology team can only be solved with the correct data research and model built before our engineers put it into working code. With teams spread across Hong Kong, Singapore, Malaysia, Indonesia, Vietnam and Taiwan, you will get to learn working in a uniquely remote team environment. Our business is growing very fast with more of these

1. Conduct data science projects to help clients drive solutions and solve business problems from various domains using a wide range of data science techniques, including but not limited to Machine learning, data visualization, statistics, and so on. 2. Design, develop and maintain scalable automated machine learning and data science pipelines and applications to empower users to extract valuable results. 3. Build new and enhance existing tools to strengthen the internal toolbox of the Data

隨著金融科技導入銀行資訊業，金融業大量的資訊宛若黃金般等待各路好手將raw data轉換成協助商業智慧, 客戶行為分析預估。種種的應用將會對金融業產生鋪天蓋地的轉型，而現在正是時候，加

Main fields of development are : Classify posts and accounts from 12 sm/fintech platforms covered in order to get nuanced sentiment (e.g. bullish bots or long-short FOMO) towards more than 20k assets (stocks, etf, crypto) Classify accounts by their professionalism and stance to certain market strategies