【外商】Data Scientist (資料科學家) #Fintech

【Who we are ?】 Hytech是一個年輕、充滿活力的團隊，專注於推動金融科技行業的企業技術轉型，是全球領先的管理技術諮詢公司。創新思維和扁平化的管理，讓團隊成員以公開、透明的方式自在工作，也為全球客戶提供卓越的商業價值服務。 【About the role - Data Scientist 】 We are seeking a Data Scientist with strong analytical and machine learning expertise to join our Product team. In this role, you will design, train, and optimize predictive models, leveraging large-scale datasets to deliver AI-driven solutions that enhance system performance and business outcomes. You will work closely with engineers, data analysts, and product managers to lead data-driven decision-making and model design, deploying predictive models into real-time product operation platforms that directly influence CTR, CVR, and revenue growth. We particularly welcome candidates who are passionate about research and technical innovation or strongly desire to continuously advance in AI/ML — to join us in driving product innovation and intelligent transformation. (我們正在尋找一位具備深厚資料分析能力與機器學習經驗的資料科學家，加入我們的產品研發團隊。在這個角色中，您將負責模型的設計、訓練與優化，處理大規模資料集，打造能直接提升產品系統效能與商業價值的 AI 解決方案。您將與工程師、資料分析師及產品經理緊密合作，主導資料驅動的決策與模型設計，並將預測模型實際部署於產品端，直接影響點擊率、轉換率與營收成長) 【身為團隊的一份子您將負責】 [Cross-functional Collaboration & Business Understanding]: 1. Engage in product and technical discussions to clarify the team/customer's requirements and data constraints, assessing the feasibility and potential risks of model development. (參與產品與技術需求討論，釐清客戶需求與資料限制，評估模型開發的可行性與潛在風險) 2. Collaborate closely with business, product, marketing, risk, and engineering teams to translate data science insights into practical, scalable, intelligent services and product solutions. (與業務、產品、行銷、風控及技術團隊緊密合作，將資料科學成果轉化為可落地的智能服務與產品解決方案) [Data Preparation & Analysis]: 1. Perform data cleaning, feature engineering, and exploratory data analysis (EDA) to ensure data integrity and model accuracy. (執行資料清洗、特徵工程與探索性資料分析，確保資料完整性與建模準確性) 2. Integrate diverse data sources (e.g., user behavior, transaction records, third-party tracking tools) to enrich data assets and analytical insights. (整合多元資料來源(如使用者行為、交易紀錄、第三方追蹤工具等)，深化數據資產應用與洞察) 3. Guide data analysts on data preprocessing and feature engineering to maintain project quality and consistency. (指導資料分析師進行資料前處理與特徵工程，確保專案執行品質與結果一致性) 4. Collaborate with data engineers and analysts to optimize data pipelines and model deployment workflows, improving data processing efficiency and system stability. (與資料工程師及分析團隊合作，優化資料管線與模型部署流程，提升資料處理效能與系統穩定性) [Model Development & Optimization]: 1. Design interpretable AI models to help business and product teams understand predictive logic, improving model transparency and trust in the application. (設計具可解釋性的 AI 模型，協助業務與產品團隊理解預測邏輯，提升模型透明度與應用信任度) 2. Develop predictive, clustering, and recommendation models; continuously monitor, evaluate, and optimize their performance and stability. (建立預測、分群與推薦模型，持續監控、評估並優化模型效能與穩定性) 3. Design robust model training and validation strategies to address challenges such as overfitting, data imbalance, and feature selection. (設計模型訓練與驗證策略，解決過擬合、資料不平衡及特徵選擇等常見挑戰) 4. Apply statistical analysis, machine learning, and deep learning techniques for data exploration and hypothesis testing, supporting product design and strategic recommendations. (應用統計分析、機器學習與深度學習技術進行資料探索與假設驗證，支持產品設計與決策建議) 5. Develop deep learning and machine learning models, leveraging large language models (LLMs) and natural language processing (NLP) tools to streamline internal workflows and enhance operational efficiency and analytical quality. (開發深度學習與機器學習模型，並運用大型語言模型及自然語言處理工具，優化內部流程、提升營運效率與分析品質) [Model Deployment & Application Strategy]: 1. Prepare detailed Statements of Work (SoW) outlining prediction tasks, data requirements, project deliverables, and acceptance criteria to ensure clear project alignment. (撰寫SoW文件，明確定義預測任務、資料需求、交付範圍與驗收標準，確保專案執行方向一致) 2. Develop model application strategies—including deployment frequency, prediction thresholds, and anomaly detection mechanisms—to continuously optimize model utilization and business impact. (制定模型應用策略(如部署頻率、預測門檻、異常警示機制等)，持續優化模型應用與商業成效)