Job Summary

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.

Main Responsibilities

A. 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.

B. 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.

C. 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 visualization techniques to generate efficient and repeatable insights.

D. Learning & Knowledge: - Stay updated on the latest trends in machine learning, AI, and analytics tools. - Participate in training programs and learning sessions to build advanced analytics capability. - Proactively seek feedback and mentorship from senior team members. - Seek out opportunities to stay current with advancements in data analytics fields. - Initiate regular meetings within the Application Development department focused on discussing progress, resolving issues, and addressing concerns related to AI and data analytics projects.

Education and Experience Requirements

- Bachler Degree University in data science, Computer Science, Mathematics, Engineering, or a related field.

- Familiarity with machine learning techniques, data wrangling, and model evaluation practices.

- High Level knowledge in data science tools like Python, R and Jupiter notebooks is desirable.