Senior Data Science Manager

· Strategic Leadership: Define and implement the data science strategy aligned with the company's overall vision and regional goals.

· Team Management: Lead and mentor a team of data scientists, ensuring high performance and professional growth.

· Data-Driven Insights to Deliver Business Value: Provide critical business insights through advanced analytics, driving value creation and informed decision-making. Deliver AA prototype products (pilots) in identified high value areas, and scale to deliver value. Manage portfolio of Advance Analytics prototype products (pilots) to identify next high value product for HEINEKEN.

· Project Oversight: Manage data science projects from inception to completion, ensuring timely delivery and alignment with business objectives. Guide data scientists to develop predictive models and only standardised value-measurement techniques (e.g. running A/B testing) to solve complex business problems, such as forecasting timeseries, prediction. Collaborate with PMO to coordinate scrums and ensure others have defined scope and clear objectives).

· Collaboration: Work with cross-functional teams to integrate data insights into various business strategies, enhancing overall efficiency.

· Innovation and R&D: Drive research and development activities to identify and develop new analytics opportunities.

· Stakeholder Communication: Act as a liaison between technical teams and non-technical stakeholders, translating complex data into actionable strategies.

· Governance and Standardization: Ensure governance and standardization of data technologies, platforms, and solutions across the region. Apply MLOps best practices to ensure the reliability and robustness of data science solutions.

· Continuous Improvement: Stay updated with the latest advancements in data science and technology to maintain a competitive edge for Data Science chapter of Heineken Asia Pacific. Continued self-development and research of best-in-class analytical approached; management and/or development and capability building of Junior/Senior Data Scientists.

· Partnership Development: Build and maintain relationships with external data and analytics partners to enhance capabilities and drive innovation.