Data Scientist

Job Description: Data Scientist (Operations & Banking)

Role: Data Scientist – Operations AI & Advanced Analytics

Location: Doha, Qatar

Industry: Banking / Financial Services

Experience Level: 5+ Years

Role Purpose

The Data Scientist will design, develop, and operationalize AI and advanced analytics solutions specifically for the Bank's Operations functions. The primary goal is to convert raw operational data into measurable business value by improving efficiency, reducing risk, enhancing SLA monitoring, and enabling predictive decision-making. This role acts as a critical bridge between Operations, Data Governance, and IT.

Key Responsibilities

1. Advanced Analytics & Modeling

• Develop predictive models to address operational challenges, including:
• SLA Breach Prediction and Workflow Bottleneck identification.
• Queue Optimization and Volume Forecasting.
• Error/Rejection Prediction in transactional processing.
• Perform deep statistical analysis and root cause modeling on operational KPIs.
• Design scoring frameworks to measure and improve operational efficiency.

2. AI & Automation Enablement

• Build and deploy AI use cases such as:
• Intelligent Workload Distribution and Document Processing Optimization.
• Anomaly Detection in transactions and Operational Risk Alerts.
• Collaborate with IT teams to productionize models and ensure scalability.
• Support the integration of AI with existing RPA (Robotic Process Automation) initiatives.

3. Dashboard & Insight Engineering

• Develop advanced Power BI dashboards that go beyond descriptive reporting.
• Implement high-value features: Predictive visuals, Trend decomposition, What-if simulations, and Scenario modeling.
• Ensure optimal data model performance and structural integrity using Advanced DAX.

4. Data Engineering & Governance Support

• Define the data structures and pipelines required for AI and machine learning use cases.
• Work closely with Data Governance to ensure Data Quality Standards, accurate historical tracking, and consistent metric definitions across timezones.

5. Performance Impact Measurement

• Quantify the success of AI initiatives by tracking:
• Cost savings and Productivity gains.
• Processing time reduction and Error rate reduction.
• Develop and maintain ROI (Return on Investment) tracking models for all Operations initiatives.

Technical Skills & Qualifications

Essential:

• Advanced Proficiency: SQL (complex joins, window functions) and Advanced DAX (Power BI).
• Modeling: Strong hands-on experience with Python or R for statistical modeling.
• Core Concepts: Deep understanding of Statistical Analysis, Process Mining, and Data Modeling (Star Schema).
• Experience: Minimum 5+ years in the Financial Services / Banking industry.

Preferred:

• Familiarity with Machine Learning libraries (scikit-learn, TensorFlow basics).
• Knowledge of LLM (Large Language Model) applications and RPA integration.
• Language: English (Mandatory); Arabic (Preferred but not mandatory).

Key Performance Indicators (KPIs)

• Number of AI/analytics use cases successfully implemented.
• Percentage reduction in SLA breaches and processing cycle times.
• Demonstrable Cost Savings and Productivity Gains.
• Accuracy rates of predictive models and adoption rates of advanced dashboards.