Data Scientist, Machine Learning & MLOps (IS-DS-ML)

Duties
ML Model Development & Deployment: Design, develop, and implement production-grade machine learning models (e.g., advanced tree-based, deep learning, clustering, and regression models) to solve critical business problems
MLOps & Pipeline Ownership: Architect, implement, and maintain robust ML Pipelines and MLOps frameworks to orchestrate seamless model development, version control, deployment, monitoring, and retraining in production environments
Business Impact & Revenue Generation: Directly drive business revenue and performance improvements by applying advanced data science techniques, ensuring all outcomes are measurable and aligned with organizational KPIs
Cross-Functional Collaboration & Storytelling: Partner closely with Marketing, Product, and executive teams to embed data-driven insights into campaign strategies and customer engagement. You will present complex findings clearly and persuasively to non-technical stakeholders, bridging the gap between deep technical analysis and business decision-making
Data Engineering for ML: Design and build efficient, scalable data pipelines using PySpark and other tools to collect, process, and transform massive, complex datasets, ensuring high data quality and accessibility for modeling
Innovation: Actively research, adapt, and apply the latest techniques and technologies in machine learning and MLOps to maintain a competitive edge

Requirements
Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related quantitative field. A Master’s degree is highly preferred
A minimum of 3 years of hands-on experience in a Data Scientist role, with significant focus on building and deploying production-level ML models
Expert-level proficiency in Python (including ML libraries), SQL, and large-scale data processing tools like PySpark
Demonstrable experience with a wide array of machine learning algorithms (e.g., XGBoost, CNNs/RNNs, sophisticated clustering methods) and deep understanding of statistical inference and model validation in a live environment
Exceptional analytical and problem-solving skills with meticulous attention to detail and a commitment to data integrity
Proven ability to work independently, manage multiple projects simultaneously in a fast-paced, dynamic environment, and drive projects to timely completion
Fluent written and verbal communication skills in English and Cantonese are essential for effective collaboration with our diverse, local business teams