Data Analytics Specialist (Cards) - 12 months

Quantum Talent Group; Saudi Arabia; On-site; SAR 180,000 - 320,000; Full time

Job summary

Quantum Talent Group is seeking an experienced Data Analytics Specialist to join a leading banking project in Saudi Arabia. You will analyze large-scale banking and card transaction data, build predictive models, and provide data-driven insights to drive growth, retention, and risk management.

You will collaborate with product teams to define KPIs and deliver actionable dashboards. The ideal candidate has 7+ years in data analytics or data science, strong banking domain experience, and

Qualifications

• 7+ years of experience in Data Analytics, Data Science, or related fields.
• Strong experience within the Banking industry, supporting Credit and Debit Card products.
• Experience in cardholder life cycle use cases such as acquisition through cross-sell, retention, portfolio spend growth, balance growth, card profitability and cards campaign design.
• Proven expertise in Data Analytics, Predictive Modeling, and Data Science.
• Experience with card portfolio analytics, transaction analysis, customer segmentation, fraud detection, credit risk, or related banking analytics functions.
• Strong proficiency in Python, SQL and SAS.
• Experience developing predictive models, customer behavior insights, and data-driven business recommendations.
• Ability to transform complex datasets into actionable insights for business stakeholders.
• Immediate availability.

Responsibilities

• Analyze large-scale banking and card transaction datasets to identify trends, opportunities, and risks.
• Develop predictive models to support business growth, customer retention, and risk management initiatives.
• Perform customer segmentation and behavioral analytics.
• Create dashboards and reports to support decision-making across business and product teams.
• Collaborate with stakeholders to define KPIs and deliver actionable insights.
• Support credit and debit card product teams through advanced analytics and data-driven recommendations.

Skills

Python

SQL

SAS

Predictive Modeling

Data Visualization

Tools

Power BI

Tableau

Job description

We are seeking an experienced Data Analytics Specialist to join a leading banking project in Saudi Arabia.

Key Requirement

• 7+ years of experience in Data Analytics, Data Science, or related fields.
• Strong experience within the Banking industry, specifically supporting Credit and Debit Card products.
• Experience in cardholder life cycle use cases such as acquisition through cross-sell, retention, portfolio spend growth, balance growth, card profitability and cards campaign design.
• Proven expertise in Data Analytics, Predictive Modeling, and Data Science.
• Experience working with card portfolio analytics, transaction analysis, customer segmentation, fraud detection, credit risk, or related banking analytics functions.
• Strong proficiency in Python, SQL and SAS.
• Experience developing predictive models, customer behavior insights, and data-driven business recommendations.
• Ability to transform complex datasets into actionable insights for business stakeholders.
• Immediate availability.

Preferred Qualifications

• Experience with credit card transaction analytics, risk scoring, churn prediction, and portfolio performance reporting.
• Strong analytical and problem-solving skills.
• Experience with data visualization and reporting tools such as Power BI or Tableau.
• Preferably currently based in Saudi Arabia.

Responsibilities

• Analyze large-scale banking and card transaction datasets to identify trends, opportunities, and risks.
• Develop predictive models to support business growth, customer retention, and risk management initiatives.
• Perform customer segmentation and behavioral analytics.
• Create dashboards and reports to support decision-making across business and product teams.
• Collaborate with stakeholders to define KPIs and deliver actionable insights.
• Support credit and debit card product teams through advanced analytics and data-driven recommendations.