Senior Manager - Data, Analytics and Artificial Intelligence (Strategy & Transformation)

Job Detail

Job Responsibilities Operations and Data Analytics •Design and optimise scalable data workflows with subject matter expertise in data loading patterns, data architecture etc. •Develop and maintain robust data pipelines that support both real-time and batch processing •Extract and integrate data from various sources, developing and scaling models in real-time business conditions. This includes interfacing (via API) between the data layer and application layer. •Leverage on SQL to ensure efficient data transformation and analytics. •Integrate diverse data sources (e.g., structured, semi-structured, and unstructured) into unified data architectures that power AI-driven applications and insights. Build and Assess Models •Train advanced models and algorithms suitable for business use – •Models include computer vision models, forecasting models, route optimisation etc. •Work as a team to ensure data readiness, feature engineering pipelines and reproducibility of experiments Present Data- driven business value of data science/ AI models •Translate model performance into clear business value (ROI, cost, efficiency, revenue). •Oversee executive‑ready dashboards that simplify complex data. •Set real‑time operational criteria to ensure models deliver measurable impact. •Lead value‑testing of new models versus legacy processes and brief executives. •Advise leadership on using unified data architectures for efficiency and advantage. •Communicate complex and black‑box models clearly to drive trust and adoption. Leadership •Champion data quality and governance by implementing validation, lineage tracking, and compliance standards. •Serve as data engineer, software engineer, cloud engineer, and AI scientist all-in-one Others •Undertake any projects or duties as directed by Management (if any) Job Requirements Familiarity with data manipulation, statistical analysis and machine learning theories. This includes extracting and integration of data from various sources, developing and scaling models in real-time business conditions. This includes interfacing (via API) between the data layer and application layer. Understanding of software engineering practices (e.g. Version Control), containerization, CI/CD pipelines Advanced proficiency in SPARK, Python, Java, SQL, Pandas, Hugging Face, Open AI APIs Advanced proficiency with both full stack software engineering and data engineering with solid experience with working with Azure etc. Familiar with data ethics and governance including best practices on how data should be managed securely Strong leadership and management skills, with the ability to motivate and develop a team.  Excellent communication and interpersonal skills, with the ability to build strong relationships with internal and external stakeholders.