Data Scientist - Credit/Debit Card Analytics

About the Role

We are seeking an experienced Data Scientist specialized in banking analytics to work onsite in Riyadh, Saudi Arabia, for a 12-month contract. The role focuses on credit and debit card data analysis, customer segmentation, and predictive modeling to enhance data-driven strategies in a banking and financial services environment.

Key Responsibilities

• Examine customer, transaction, and portfolio data to detect business opportunities and market trends.
• Create customer segmentation models utilizing behavioral, transactional, and demographic information.
• Design and implement predictive models addressing customer behavior, retention, acquisition, and sales opportunities such as cross-selling and upselling.
• Analyze credit card portfolio metrics including customer spending patterns, revenue, and profitability.
• Support analysis of marketing campaigns by measuring their performance and return on investment.
• Discover ways to boost customer engagement, increase revenue, and improve portfolio outcomes.
• Conduct statistical analyses and transform insights into practical business strategies.
• Collaborate intensively with teams from business, product management, marketing, and risk assessment.
• Develop detailed reports and dashboards to effectively present analytical findings.

Required Skills and Experience

• Demonstrable experience in banking or financial services analytics.
• Expertise in credit and debit card data analytics.
• Proficiency in developing customer segmentation techniques.
• Strong background in predictive modeling and machine learning methods.
• Skilled in customer and portfolio analytics.
• Experience with revenue and profitability analysis.
• Advanced SQL abilities.
• Proficient in Python programming.
• Experience handling transaction and customer data.
• Excellent analytical skills coupled with strong communication abilities for liaising with stakeholders.

Additional Preferred Competencies

• Knowledge of Customer Lifetime Value (CLV) modeling.
• Experience with churn or attrition prediction models.
• Propensity and campaign analytics expertise.
• Familiarity with cross-sell, upsell, next best offer/action techniques.
• Spend and credit risk analytics understanding.
• Skills in statistical modeling.
• Experience using data visualization tools such as Power BI or Tableau.
• Familiarity with PySpark technology.