Job Description: Business Analyst – Corporate Banking (AI/ML Focus)
Location: Abu Dhabi, UAE
Company: BigTapp Analytics
Client: Tier-1 Banking Institution
Role Overview
BigTapp is seeking a highly skilled Business Analyst with deep Corporate Banking Subject Matter Expertise (SME)to join our project team in Abu Dhabi. This role serves as the critical bridge between the client bank’s senior business stakeholders and our technical delivery teams (Data Engineers and Data Scientists).
You will be embedded within a leading bank, working directly with the bank's business stakeholders across Corporate & Institutional Banking divisions to elicit, define, and document Machine Learning use cases that drive value across Corporate Banking domains, including Customer Experience, Revenue Optimization, Risk Management, and Profitability.
Your outputs will serve as the definitive brief for BigTapp's data engineering and data science teams who will build the models and pipelines.
This role sits at the intersection of banking domain knowledge and applied data science, requiring equal fluency in boardroom-level business dialogue and technical ML conceptualisation. You will act as the authoritative voice of the business inside the delivery team, ensuring that every model built reflects real commercial intent and can be measured against meaningful banking KPIs.
Key Responsibilities
Stakeholder Engagement: Facilitate workshops and interviews with Corporate Banking business heads, relationship managers, and product owners to identify business pain points and opportunities for AI/ML intervention.
Use Case Development: Translate high-level business requirements into detailed functional specifications for ML models (e.g., Churn Prediction, Next Best Action, Credit Risk Scoring, Revenue Forecasting).
Domain Expertise: Provide expert guidance on Corporate Banking workflows, including Trade Finance, Cash Management, Lending, and Treasury services.
Technical Documentation: Create comprehensive "Use Case Blueprints" that include data requirements, success metrics (KPIs), and business constraints for the technical team.
Bridging the Gap: Explain complex Data Science concepts to non-technical business users while ensuring Data Scientists understand the nuances of banking data and regulatory constraints.
Value Assessment: Analyze the impact of proposed AI solutions on the bank’s Profit & Loss (P&L), focusing on revenue leakage, cost reduction, and enhanced risk mitigation.
UAT & Validation: Oversee User Acceptance Testing (UAT) to ensure the developed AI/ML models meet business objectives and are integrated seamlessly into the bank’s existing workflows.
· Serve as the primary liaison between the client bank's business lines and BigTapp's technical delivery team throughout the model development lifecycle.
· Manage stakeholder expectations, resolve conflicting requirements, and escalate roadblocks to the Engagement Lead.
· Present use case progress, model insights, and recommendations to senior bank leadership in a clear, non-technical manner.
· Contribute to BigTapp's internal knowledge base, methodology guides, and use case templates.
· Track and report on post-deployment model performance against defined banking KPIs and recommend refinements.
· Stay current with emerging ML applications in corporate banking globally and champion their applicability within the engagement.
Domain Coverage Required
Domain
Example ML Use Cases
Customer Experience
Client sentiment analysis, relationship health scoring, next-best-action engines, churn prediction, digital channel adoption propensity.
Revenue & Sales
Wallet share estimation, cross-sell / upsell propensity, pricing optimisation, trade finance opportunity identification, deal win probability.
Credit & Risk
Corporate PD/LGD model enrichment, early warning systems for credit deterioration, covenant breach prediction, collateral valuation anomaly detection.
Profitability
Client-level profitability forecasting, fee leakage detection, cost-to-serve modelling, RAROC optimisation, RWA forecasting.
Fraud & Compliance
Transaction monitoring anomaly detection, KYC risk scoring, sanctions screening optimisation, internal fraud indicators.
Operations
Credit decisioning automation, document classification (trade finance), workflow prioritisation, SLA breach prediction.
Required Skills & Experience
1. Banking Domain Expertise
Minimum 7–10 years of experience in Corporate Banking.
Deep understanding of the corporate customer lifecycle: Onboarding, Relationship Management, Credit Assessment, and Retention.
· Demonstrated hands-on coverage of at least three of the following areas: corporate credit, relationship management, trade finance, cash management, treasury, corporate finance, risk management, or financial planning & analysis.
· Strong understanding of the UAE and wider GCC banking landscape, regulatory environment (CBUAE), and corporate client segments (large corporates, mid-market, government-related entities).
· Familiarity with core banking systems, credit workflows, and data infrastructure typical of regional banks.
Strong knowledge of banking financial drivers: Net Interest Margin (NIM), Risk-Weighted Assets (RWA), and Customer Lifetime Value (CLV).
2. Analytical & Technical Proficiency
Proven experience in Business Analysis within a data-driven environment.
Familiarity with Data Science concepts: You should understand the difference between supervised/unsupervised learning, the importance of data labeling, and how model accuracy impacts business decisions.
· Experience producing structured functional specifications, user stories, or analytical briefs consumed by technical teams.
Ability to interpret data schemas and collaborate with Data Engineers on data sourcing from core banking systems.
3. Communication & Leadership
Exceptional documentation skills (BRDs, FSDs, and Use Case definitions).
Ability to influence senior stakeholders and manage expectations in a high-pressure consulting environment.
Fluent in English; knowledge of the regional banking landscape in the GCC is highly preferred.
· Proven track record in requirements elicitation, stakeholder management, and documentation in complex, multi-stakeholder environments.
· Strong facilitation skills; able to run workshops with senior banking executives and translate outputs into actionable deliverables.
Preferred Qualifications
Bachelor’s or Master’s degree in Finance, Economics, Business Administration, or a related field.
Certifications in Business Analysis (CBAP) or introductory certifications in Data Science/AI.
Previous experience working on digital transformation or AI-led projects within a consulting or banking framework.
Prior experience in working with UAE or GCC headquartered banks is preferred.
· Exposure to AI/ML delivery projects within a banking context, either as a business sponsor, product owner, or domain consultant.