Staff Data Scientist

Role Overview

Staff Data Scientist at RAK BANK. The role leads the development of AI/ML solutions that drive data-driven decision-making across customer engagement, marketing, retention, risk, and product functions.

Role Purpose

Lead the development and deployment of AI/ML solutions that enable data-driven decision-making across the organization. Translate business problems into analytical use-cases, build and operationalize machine learning models, and ensure high-quality delivery with effective stakeholder communication and adherence to reproducible, scalable data science practices.

Key Responsibilities

Strategy & Use-Case Development

• Translate business problems into analytical use-cases with defined outcomes in collaboration with the Data Science team.
• Identify and streamline AI/ML use-cases across various business units and stakeholders.
• Drive innovation by enhancing existing solutions and designing new ones.

Model Development & Implementation

• Develop, implement, and test appropriate algorithms for given use-cases.
• Build end-to-end AI/ML models from development to deployment.
• Demonstrate strong conceptual understanding of machine learning algorithms including multi-variate regressions, classification algorithms, time series techniques, clustering, NLP, Image Processing, and optimization models.
• Deploy models in both batch and real-time environments.

Collaboration & Stakeholder Management

• Work closely with Business Analysts for requirement gathering and understanding.
• Partner with Data Engineers to build data pipelines and automate production-alize complex ML models into insights and recommendations.
• Communicate effectively with stakeholders on delivery planning and model usage.
• Build collaboration and awareness in the bank's analytics community.
• Ensure efficient usage of models by the business and provide recommendations for improving decision touchpoints.

Code Quality & Standards

• Ensure high coding standards and designing standards to ensure reproducibility.
• Conduct peer review and maintain transparency in work with the line of reporting.

Analytics & Reporting Support

• Support ad-hoc requirements in terms of MIS development.
• Build and analyze SAS Data Models.
• Streamline the reporting process through various reporting and analytical tools.

Qualifications & Experience

• Overall 7+ years of experience in analytics, data science, or similar function.
• 3–4 years of experience in banking analytics.
• Experience working on end-to-end ML model deployment.
• Experience in Machine Learning, Deep Learning, Time-Series, Optimization, and Customer Analytics.

Skills & Competencies

• Expert-level proficiency in Python.
• Strong SQL experience.
• SAS, R, or Spark experience as a plus.
• Knowledge of Retail Banking Products.
• Deployment experience across various databases, server and cloud environments including AWS, Azure, APIs, ODBCs, and web apps.
• Excellent Banking Functional Knowledge, noted as a major plus.
• Good written and oral communication skills.
• Strong documentation skills with ability to communicate effectively with stakeholders.
• Hands-on experience with open-source technologies.