Senior Data Scientist

Senior Data Scientist - Job Description Who we are Mico is a company with a clear mission: to Empower every brand by building lifetime trust through humanlike technology. This ambitious goal sets the stage for our vision and core values. By 2030, Mico aims to be the Asia No.1 Brand Empowerment Company. This mid-term goal outlines our dedication to becoming the leading force in empowering brands across Asia. To achieve our mission, we identify four key values: WOW THE CUSTOMER SMART SPEED OPEN MIND ALL FOR ONE Job Summary The Senior Data Scientist will work on data-driven initiatives to solve complex business challenges, leveraging advanced analytics, machine learning, and statistical modeling. This role requires expertise in translating data insights into actionable strategies and collaborating with cross-functional teams. Ideal candidates will have a strong background in analytics or tech-driven industries. Key Responsibilities KPI Design & Stakeholder Strategy: Partner with cross-functional stakeholders (e.g., Marketing, Finance) to define and propose business KPIs that are logically sound, reasonably challenging, and easily communicable to non-technical teams. Data Engineering & EDA: Clean, preprocess, and validate large, complex datasets (structured/unstructured). Perform deep-dive Exploratory Data Analysis (EDA) to identify patterns, ensure data integrity, and set the initial strategic direction for analysis. Personalization & Recommendation: Design and implement recommendation engines to enhance user engagement and brand trust, leveraging both classical and deep learning-based approaches. Iterative Model Development: Develop and deploy predictive models—including customer behavior prediction, customer lifetime value (CLV), media mix modeling, and time-series forecasting—using Python and PyTorch. Advanced Segmentation: Lead customer behavioral analysis projects using unsupervised clustering techniques to drive personalized brand empowerment strategies. Continuous Improvement Loops: Systematically identify bottlenecks in model accuracy through rigorous evaluation and error analysis; iterate on model architectures and data features to consistently hit and exceed target KPIs. Collaborative Engineering & Privacy: Maintain production-grade code repositories using GitHub, ensuring version control and documentation are integrated into the R&D process while adhering to data privacy and ethical AI practices. Cutting-Edge Research: Research and implement state-of-the-art techniques, including LLMs/Generative AI, NLP, and Deep Learning, to enhance business strategies and solve brand empowerment challenges. Required Qualifications Education: Master’s/PhD in Statistics, Computer Science, Econometrics, or related quantitative fields. Experience: 5+ years in data science, with proven expertise in: End-to-End Ownership: Proven experience leading projects from initial data preparation and KPI definition through to delivery of final business impact. Programming: Expert-level proficiency in Python, SQL, and Spark, along with deep practical knowledge of libraries such as Pandas, Scikit-learn, PyTorch, and PySpark. Modeling & Mathematical Expertise: Extensive experience in the architecture and implementation of Decision Trees, Regression models, and Deep Learning (including LLMs/NLP). Recommendation Systems: Deep expertise in building recommendation models, including Collaborative Filtering, Content-Based Filtering, Matrix Factorization, and Neural Collaborative Filtering (NCF). Analytical Methods: Strong command of Unsupervised Clustering/Segmentation techniques and modern Time-Series Forecasting methodologies. Experimental Methodology: Rigorous skills in offline evaluation, cross-validation techniques, and iterative hypothesis testing to improve model performance. Cloud Platforms: Hands-on experience architecting and deploying solutions in Azure, Databricks, Snowflake, or AWS. Development Environment & Version Control: Advanced proficiency with Linux/Unix environments and VS Code, alongside GitHub/Git for collaborative development, branching strategies, and CI/CD workflows. Soft Skills: Exceptional stakeholder management (translating complex data into simple stories), professional time management, and a relentless problem-solving mindset.