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  Define and implement enterprise-wide Data Science and AI strategies and initiatives aligned with business priorities.
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  Advise C-level executives on opportunities to leverage data to drive business growth, mitigate risks, and improve operational efficiency.
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  Expand the adoption and best practices of AI across subsidiary companies.

2. AI Model Development and Innovation

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  Lead the development of Predictive AI solutions, including forecasting, scoring, optimization, recommendation, and anomaly detection, with strong data/feature engineering and model lifecycle management.
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  Lead Generative AI initiatives, including LLMs, RAG, agents, and domain fine-tuned models, covering prompt frameworks, knowledge integration, and safety guardrails.
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  Architect model solutions by selecting the most suitable approach among build, fine-tune, RAG, prompt engineering, or third-party models, based on business, cost, risk, and performance trade-offs.
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  Oversee MLOps and LLMOps pipelines, ensuring scalable deployment, versioning, CI/CD, monitoring, and automation for both predictive and generative models.
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  Create reusable AI assets and platforms, including feature stores, vector databases, prompt libraries, embeddings, model registries, inference APIs, and agent orchestration frameworks.

3. Technology Architecture

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  Collaborate with the IT Division and Data Architecture teams to build scalable data platforms, pipelines, and AI/ML infrastructure for both ML and GenAI, supporting both batch and real-time flows.
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  Drive experimentation and R&D to stay aligned with practical emerging AI technologies.

4. Governance and Compliance

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  Establish ethical AI practices and ensure compliance with data privacy, regulatory, and security requirements.

5. Business Partnership

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  Partner with business units to provide consultation on the application of AI/GenAI for business needs.
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  Collaborate with business and product owners to define problem statements, estimate value, build ROI and impact models, and measure post-deployment outcomes.

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  Provide the necessary leadership and management to enable direct and indirect subordinates to achieve the goals of the AI Factory.
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  Conduct headcount planning and human resource allocation, while collaborating with HR to develop recruitment, training, succession planning, career development roadmaps, employee recognition, and performance management for employees within the Department.
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  Develop talent and organizational capabilities in AI/GenAI by providing coaching and leadership to team members and building future leaders within the organization.

7. Corporate Culture

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  Act as a role model in building corporate culture.
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  Ensure the correct and full implementation of corporate culture plans.
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  Ensure employees understand and consistently apply VPBank’s corporate cultural values and behavioral standards.

Requirements

1. Educational Qualifications

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  Master’s degree or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or a related field.

2. Relevant Knowledge / Expertise

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  Minimum 10 years of experience in data science or advanced analytics, including at least 5 years in a leadership role.
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  Proven track record in building and deploying machine learning and AI models in large-scale production environments.
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  Expertise in statistical modeling, machine learning, deep learning, natural language processing, and big data technologies.

3. Skills and Competencies

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  Proficiency in programming languages such as Python, R, and SQL, with experience in distributed computing technologies including Spark and Hadoop, as well as cloud platforms such as AWS, GCP, and Azure.
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  Strong leadership, stakeholder management, and communication skills, with the ability to influence at the executive level.