AVP- Data Scientist

First Abu Dhabi Bank; Abu Dhabi; On-site; AED 200,000 - 300,000; Full time

Job summary

A leading bank in the UAE is seeking a Data Scientist to design and implement machine learning models within personal banking. The ideal candidate will have over 5 years of experience in data science, particularly in a banking environment, along with strong skills in SQL and Python. This role offers the opportunity to contribute to innovative data products and engage with senior stakeholders to drive business insights.

Qualifications

• 5+ years of applied data science experience in a banking environment.
• Proven track record of deploying production-grade models with ongoing performance management.
• Strong hands-on skills in SQL, Python, and ML libraries.
• Demonstrated experience in feature engineering and large-scale data handling.

Responsibilities

• Design and build machine learning models aligned to core banking priorities.
• Monitor model performance and lead retraining or recalibration processes.
• Implement production-ready model pipelines using CI/CD tooling.
• Present modelling outcomes and insights to non-technical stakeholders.

Skills

Machine Learning

Data Analysis

SQL

Python

Stakeholder Engagement

Education

Bachelor's or Master's degree in a quantitative field

Tools

scikit-learn

XGBoost

Dataiku

Databricks

Azure

AWS

GCP

Job description

• Division: Personal, Wealth & Business Banking

Company Description

Looking to join the #1 bank in the UAE and one of the most prestigious in the region? We're looking for professionals who are driven, skilled, and ready to take on new challenges. Together, we can achieve our goals, making a lasting impact on both our company and the industry as a whole.

Join us and leave your mark on the industry. Let's work together to achieve great things and unlock new possibilities for our employees, customers, and communities.

Job Description

1. Advanced Analytics Product Development

• Design and build machine learning models aligned to core personal banking priorities
• Identify commercially viable use cases in collaboration with product, marketing, and business stakeholders
• Drive the development of analytics products that are scalable, measurable, and operationally robust

2. Model Development and Lifecycle Management

• Build, test, and deploy machine learning models into production environments
• Ensure models are aligned with Model Risk Management standards and delivery governance
• Monitor model performance and lead retraining or recalibration processes as needed

3. MLOps and Operationalisation

• Implement production-ready model pipelines using CI/CD tooling and automated monitoring
• Ensure continuity of performance and data integrity throughout the lifecycle

4. Feature Engineering and Data Exploration

• Lead the extraction and transformation of raw data into high-quality features
• Conduct deep EDA to identify trends, correlations, and value-driving insights
• Understand and navigate complex banking datasets including transactional, behavioural, and product data

5. Business Engagement and Communication

• Present modelling outcomes and insights to senior non-technical stakeholders with clarity and precision
• Translate business opportunities into concrete data science initiatives
• Provide clear recommendations and options based on data-driven insights

6. Innovation and Delivery Focus

• Develop and prototype new modelling techniques and innovative data products with a commercial lens
• Prioritise delivery and measurable value over theoretical perfection

Contribute to the development of reusable frameworks and accelerators to optimise delivery methodologies for data science teams

Qualifications

Required Experience and Skills:

• 5+ years of applied data science experience in a banking environment
• Proven track record of deploying production-grade models with ongoing performance management
• Strong hands-on skills in SQL, Python, and ML libraries (e.g. scikit-learn, XGBoost)
• Demonstrated experience in feature engineering and large-scale data handling
• Familiarity with MLOps pipelines and tooling for monitoring and automation
• Strong commercial acumen and ability to scope and deliver high-impact use cases
• Excellent presentation, communication, and stakeholder engagement skills
• Deep understanding of model governance standards and regulatory expectations

Preferred Qualifications:

• Experience with platforms such as Dataiku and Databricks is a significant bonus
• Proven experience with statistical modelling mastery
• Bachelor's or Master's degree in a quantitative field (e.g. Computer Science, Statistics, Engineering)

Experience working in cloud environments (Azure, AWS, or GCP)

Job Location