QNB3829 - Associate Data Science (Qatarization

About QNB

Established in 1964 as the country’s first Qatari‑owned commercial bank, QNB Group has steadily grown to become the largest bank in the Middle East and Africa (MEA) region.

Established in 1964 as the country’s first Qatari‑owned commercial bank, QNB Group has steadily grown to become the largest bank in the Middle East and Africa (MEA) region. QNB Group’s presence through its subsidiaries and associate companies extends to more than 31 countries across three continents providing a comprehensive range of advanced products and services. The total number of employees is more than 28,000 serving up to 20 million customers operating through 1,000 locations, with an ATM network of 4,300 machines.

QNB has maintained its position as one of the highest‑rated regional banks from leading credit rating agencies including Standard & Poor’s (A), Moody’s (Aa3) and Fitch (A+). The Bank has also been the recipient of many awards from leading international specialised financial publications. Based on the Group’s consistent strong financial performance and its expanding international presence, QNB currently ranks as the most valuable bank brand in the Middle East and Africa, according to Brand Finance Magazine.

QNB Group has an active community support program and sponsors various social, educational and sporting events.

The Associate, Data Science plays a foundational role in supporting data science initiatives across the bank. The role involves working under the guidance of senior team members to build and deploy machine learning models, conduct exploratory data analysis, and generate data‑driven insights to solve business problems. The associate collaborates with business units to understand requirements, supports model development, and contributes to building scalable data science assets that align with QNB’s strategic goals.

• Shareholder & Financial:
  
  - Support the development and tracking of KPIs to measure impact of data science initiatives.
  
  - Assist in delivering data analytics solutions that drive business efficiency and revenue growth.
  
  - Take part in the overall QNB Data & Analytics strategy execution.
  
  - 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.
• Customer (Internal & External):
  
  - Work with internal stakeholders to understand business needs and translate them into data problems.
  
  - Assist in preparing clear, concise, and insightful reports or dashboards for business users.
  
  - Support model testing and deployment activities, ensuring stakeholder alignment and timely delivery.
  
  - Ensure activities are conducted in accordance with Service Level Agreements (SLAs) with internal departments and units to achieve significant improvements in turnaround times.
  
  - Provide precise and timely data to external and internal auditors, compliance teams, financial control, and risk management when required.
  
  - 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.
• Internal (Processes, Products, Regulatory):
  
  - Support the AVP and other senior data scientists in developing machine learning and statistical models.
  
  - Prepare and clean large datasets for analysis and modelling purposes.
  
  - Contribute to the implementation of AI/ML pipelines and production‑ready solutions.
  
  - Document methodologies, outcomes, and learnings for reproducibility and scaling.
  
  - 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…