QNB- Assistant Vice President, Data Science

Overview

The data scientist plays a crucial role in driving advanced analytics and AI initiatives across the bank. This role collaborates closely with business and operational leaders to provide expert support, leveraging data analytical and AI tools to deliver strategic insights and actionable recommendations. Through hypothesis-based analysis to machine learning (ML) projects and applying Large Language Models, the data scientist models complex business problems using a variety of techniques including Predictive or prescriptive analytics, deep learning, GenAI and visualization.

Additionally, they support senior leadership by overseeing the development of business insights, reports, and AI capabilities to inform decision-making processes and drive impactful outcomes.

• A. Shareholder & Financial:
  
  - Implement Key Performance Indicators (KPIs) and monitor the performance of Data & AI-driven solutions to measure their impact on business outcomes.
  
  - Promote cost consciousness and efficiency in Data Analytics and AI projects to minimize expenses and maximize returns.
  
  - Implement Key Risk Indicators (KRIs) and manage the Group’s exposure to Data & AI related risk effectively accordingly.
  
  - Coordinate and obtain approval from the EVP Data & Analytics for projects and new systems that would impact capital or operating expenditures.
  
  - Take part in the overall QNB Data & Analytics strategy execution.
  
  - Communicate comprehensive and cost-effective Data, Analytics & AI solutions to user requirements keeping in mind the Group’s budgets and targets while enhancing productivity, to minimise cost, avoid waste, and optimise benefits for the bank.
  
  - Promote cost consciousness and efficiency and enhance productivity, to minimise cost, avoid waste, and optimise benefits for the bank.
  
  - Act within the limits of the powers delegated to the incumbent.
• B. Customer (Internal & External):
  
  - Collaborate closely with peers, key divisional stakeholders, and third-party support teams to identify and implement advanced data-driven and AI solutions that deliver substantial value for QNB and its customers.
  
  - Translate complex business needs into comprehensive data science and analytics requirements to support strategic business decision-making.
  
  - Provide strategic guidance regarding the potential and implementation of AI and data science within the organization.
  
  - Assist customers with thorough and insightful responses to their inquiries about the Bank’s products and strive to provide innovative solutions to their requests.
  
  - Ensure activities are conducted in accordance with Service Level Agreements (SLAs) with internal departments and units to achieve significant improvements in turnaround times.
  
  - Build and maintain robust and effective relationships with related departments and units to achieve the Group’s overarching objectives.
  
  - Provide precise and timely data to external and internal auditors, compliance teams, financial control, and risk management when required.
  
  - To assist customers in all their queries on Bank’s product and seek solution to their requests.
  
  - Maintain activities in accordance with Service Level Agreements (SLAs) with internal departments/units to achieve improvements in turn-around time.
  
  - Build and maintain strong/effective relationships with related departments/units to achieve the Group’s objectives.
  
  - Provide timely/accurate data to external/internal Auditors, Compliance, Financial Control and Risk when required.
• C. Internal (Processes, Products, Regulatory):
  
  - Develop predictive models using a variety of advanced machine learning techniques to extract meaningful insights.
  
  - Research and implement cutting-edge techniques and tools in data analytics and artificial intelligence to streamline data analysis processes and enhance decision-making.
  
  - Identify and implement new statistical or mathematical methodologies as required for specific data-driven projects.
  
  - Build robust data-driven models to address complex business questions and employ large-scale experimentation, analysis, and visualization techniques to generate efficient and repeatable insights.
  
  - Integrate domain knowledge into AI…