Company Description WD is building the infrastructure behind the AI-driven data economy. As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time.
The Senior ML Engineer (MLOps) owns the transition from ad-hoc ML deployments to a registered, monitored, governed ML platform — the lifecycle every data scientist and ML practitioner across the company uses. The role also curates
Position Summary The Senior Data Scientist plays a key role in designing and delivering AI, machine learning, and automation solutions that drive business impact across retail and digital transformation initiatives. This role works closely with business
The Senior Data Governance Specialist is the senior individual contributor who builds and runs the data-governance tooling that makes the program real — the PDPA compliance scanner, the data-catalogue automation, the access-control sync, the AI model-governance
Requirements Proficiency in Python and PySpark for data analysis, machine learning, and demand forecasting (regression + time series models) Experience with forecasting models such as LightGBM/ XGBoost and classical time series methods (e.g., ETS/ARIMA) Strong understanding of the
Company Description At WD, our vision is to power global innovation and push the boundaries of technology to make what you thought was once impossible, possible. At our core, WD is a company of problem solvers.
The Senior Data Governance Specialist is the senior individual contributor who builds and runs the data-governance tooling that makes the program real — the PDPA compliance scanner, the data-catalogue automation, the access-control sync, the AI model-governance
Position Summary The Senior Data Scientist plays a key role in designing and delivering AI, machine learning, and automation solutions that drive business impact across retail and digital transformation initiatives. This role works closely with business
The Senior ML Engineer (MLOps) owns the transition from ad-hoc ML deployments to a registered, monitored, governed ML platform — the lifecycle every data scientist and ML practitioner across the company uses. The role also curates
Requirements Proficiency in Python and PySpark for data analysis, machine learning, and demand forecasting (regression + time series models) Experience with forecasting models such as LightGBM/ XGBoost and classical time series methods (e.g., ETS/ARIMA) Strong understanding of the
The ML Engineer builds the classical retail-ML cores that power the highest-stakes agents on an AI-native retail decisioning platform — demand forecasting that must beat a legacy system, replenishment and allocation models, causal-insight models for executive