Key Responsibilities
Data Strategy & Leadership
- Define and execute the enterprise-wide data & analytics strategy aligned with the bank's business priorities.
- Build and lead a high-performing team of data scientists, data engineers, and BI specialists.
- Promote a strong data-driven culture across the organization.
Data Governance & Management
- Establish data governance frameworks, policies, and standards to ensure data quality, security, and compliance with local and international regulations (e.g., SBV, Basel, GDPR).
- Oversee data architecture, data warehousing, and master data management.
Analytics & Insights
- Drive the adoption of advanced analytics, predictive modeling, and AI/ML solutions to support risk management, fraud detection, credit scoring, and customer personalization.
- Translate complex data insights into actionable recommendations for senior management.
Collaboration & Stakeholder Engagement
- Partner with business leaders (Risk, Finance, Operations, Marketing, Digital Banking) to identify data-driven opportunities.
- Work closely with IT to modernize data platforms (cloud, big data, real-time analytics).
Innovation & Continuous Improvement
- Stay updated with emerging technologies (AI, GenAI, cloud data platforms, open banking).
- Drive proof-of-concepts (POCs) and scale successful innovations into production.
Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Finance, or related field.
- 12+ years of experience in data analytics, business intelligence, or data science, with at least 5 years in a leadership role.
- Strong expertise in data governance, data architecture, and regulatory compliance for banking/financial services.
- Proven track record of delivering large-scale data initiatives in banking, fintech/ecommerce, or financial institutions.
- Proven experience in building data solutions from scratch (data lake, data lakehouse, data warehouse).
- Strong expertise in Azure cloud services and Databricks.
- Hands-on experience in migrating legacy systems to modern data platforms.
- Solid background in data governance, with focus on data security and data quality management.
- Experience in end-to-end ML product implementation is a strong plus
- Hands-on knowledge of analytics tools & platforms (SQL, Python, R, Tableau, Power BI, Hadoop, Spark, cloud platforms).
- Excellent leadership, communication, and stakeholder management skills.
- Strong business acumen with the ability to link data initiatives to business outcomes.
