Principal Data Scientist

Role Overview

Principal Data Scientist at RAK BANK, based in the United Arab Emirates. This role leads the development and deployment of advanced machine learning solutions that power personalized customer decisioning while driving best-in-class modeling, MLOps, and data engineering practices.

Role Purpose

Lead the development and deployment of advanced machine learning solutions that power personalized customer decisioning. The role combines hands-on technical expertise with leadership to deliver scalable models, robust pipelines, and high-impact insights across the organization.

Key Responsibilities

Modeling & Customer Decisioning

• Build and deploy propensity models for cross-sell and upsell initiatives.
• Design and optimize Next Best Action and Next Best Offer engines.
• Apply supervised learning, uplift modeling, and causal inference techniques.

MLOps & Production Deployment

• Deploy models to production via APIs or streaming systems including Kafka and Flink.
• Establish model versioning, experiment tracking, and deployment using MLflow, SageMaker, or Vertex AI.
• Establish and maintain production-grade ML pipelines using tools like Airflow, MLflow, or Kubeflow.

Monitoring & Observability

• Set up model monitoring, drift detection, and alerting systems using Prometheus, Grafana, Evidently, or custom dashboards.
• Implement logging frameworks and performance profiling for ML services.

Data Infrastructure & Engineering

• Optimize data infrastructure across cloud platforms including AWS, GCP, or Azure.
• Manage data warehouses such as Snowflake, BigQuery, or Redshift.
• Optimize SQL queries and manage large datasets effectively.

Leadership & Collaboration

• Lead senior data scientists while maintaining hands-on technical involvement.
• Partner with product managers, engineers, and business stakeholders on aligned initiatives.

GenAI Integration

• Integrate GenAI capabilities into decisioning systems and customer-facing products.
• Work with large language models, embeddings, prompt engineering, and vector databases including FAISS and Pinecone.

Qualifications & Experience

• Bachelor's degree in Econometrics, Actuarial Science, Mathematics, Statistics, Computer Science, or a related field.
• 10 years' experience in a senior data scientist position.
• Modeling experience including knowledge of cloud platforms and GenAI.
• Strong analytics experience in the banking sector.

Skills & Competencies

• Expert-level Python coding (mandatory).
• Familiarity with R or Scala (preferred).
• Distributed computing using Spark, Dask, or Ray.
• Software engineering best practices including version control, CI/CD, and testing.
• Advanced SQL skills with query optimization capability.
• Cloud platform experience with AWS, GCP, or Azure.
• Data warehouse management using Snowflake, BigQuery, or Redshift.
• Streaming systems experience with Kafka and Flink.
• Model monitoring and drift detection tools.
• Large language models and vector database knowledge.
• Leadership ability with senior-level technical staff.
• Cross-functional collaboration with product, engineering, and business teams.

Additional Information

• Competitive and performance-linked compensation.
• Diverse workforce and inclusive organizational culture.
• Career development and growth opportunities by design.
• Opportunity to work with senior minds in the field.
• Commitment to fostering innovation, growth, and excellence across the organization.