Careers at Diamond Trust Bank (DTB)

Job Purpose:

Lead the design and deployment of cutting‑edge machine learning and statistical models that power the bank’s most critical decisions across credit, fraud, customer management, marketing, and operations. Champion innovation within DTB’s risk and analytics ecosystem—driving advancements in credit scoring, alternative data modelling, forecasting, and real‑time decisioning. Your work will strengthen model accuracy, uphold regulatory compliance, and deliver measurable business impact, positioning data and AI at the heart of DTB’s digital evolution.

Key Responsibilities:

Credit-Risk & Lending Analytics (Primary)

• Lead development of credit-risk models:
  • Application & behaviour scorecards
  • PD/LGD/EAD models (Basel & IFRS9)
  • Credit limit assignment & pricing models
  • Champion–challenger frameworks
  • Build decision engines and real-time scoring capabilities.
• Oversee model monitoring, backtesting, calibration, and governance.

Customer & Product Analytics

• Develop customer lifetime value (CLV) models, churn prediction, segmentation models, and recommendation systems.
• Support pricing optimization for lending & deposits.
• Build models for product cross-sell, upsell, and next-best-action (NBA).

Fraud & Financial Crime ML

• Develop anomaly-detection, fraud detection, and real-time transaction scoring models.
• Implement behavioural biometrics and device-risk models.
• Work closely with Financial Crime & Cybersecurity teams to operationalize models.

Marketing, Personalization & CVM Analytics

• Build targeting models, propensity models, campaign uplift models, and customer segmentation.
• Partner with CVM team to automate customer journeys with ML-driven triggers.

Operational & Forecasting Models

• Forecast loan demand, deposits, NPL trajectories, collections performance, and cash flows.
• Work with Finance on balance-sheet forecasting and stress-testing scenarios.

NLP, Generative AI & Automation

• Develop NLP models for call-centre transcripts, customer messages, chatbots, and complaint classification.
• Implement GenAI for document classification, summarization, and knowledge discovery.
• Guide safe AI adoption, model governance, and prompt engineering.

Data Engineering & Big Data

• Build scalable pipelines using Spark, Hadoop, Kafka, Airflow.
• Collaborate with data engineering on feature stores, ML pipelines, and model CI/CD.

Leadership & Governance

• Mentor data scientists and analysts.
• Lead model governance sessions with Internal Audit, Model Risk, and Regulators.
• Translate complex models into actionable strategies for business leaders.

Qualifications & Experience:

• Advanced academic strength — a master’s degree in Statistics, Machine Learning, Data Science, Applied Mathematics, or Computer Science is highly preferred, showcasing your depth in analytical and quantitative disciplines.
• Proven leadership in data science — 7–12+ years of hands‑on experience building advanced models, including 5+ years specifically in banking credit risk, credit scoring, or regulatory modelling.
• Technical excellence — mastery of Python, SQL, Spark, and modern MLOps tools such as MLflow and Docker, with demonstrated experience implementing machine‑learning solutions at big‑data scale.
• Regulatory and risk expertise — strong, practical knowledge of IFRS9, Basel standards, and CBK model governance requirements, enabling you to build models that are both high‑performing and fully compliant.

Key Competencies

• Expertise that blends deep risk‑modelling mastery with versatile, modern machine‑learning skills, enabling you to build robust, scalable, and intelligent decisioning systems.
• Exceptional communication and storytelling ability, with the confidence to engage C‑suite leaders, influence strategic direction, and clearly articulate model insights to regulators.
• A strong strategic mindset, ensuring every model, feature, and analytical framework directly supports the bank’s business priorities, customer needs, and risk appetite

go to method of application »

Job Purpose:

In this role, you will design, build, and optimize the data engines that power DTB’s intelligence. You will develop robust data pipelines, feature stores, model‑serving systems, and scalable big‑data platforms that enable advanced credit scoring, fraud detection, customer intelligence, and a wide range of machine‑learning applications.

You will be at the heart of transforming DTB into a data‑driven organization—ensuring that teams across the bank can rely on high‑quality, trusted, and scalable data to drive smarter decisions, stronger governance, and innovative digital solutions. This is a high‑impact role for a builder, a problem‑solver, and a visionary ready to shape the future of data and AI at DTB

Key Responsibilities:

 Science & ML

• Build and maintain ETL/ELT pipelines that feed modelling datasets from multiple banking systems (CBS, LMS, CRM, Cards, Mobile Banking, Bureau, Collections systems).
• Develop automated data preparation workflows for credit scoring, fraud models, behavioral models, and IFRS9 modelling.
• Create end-to-end ML pipelines integrating feature engineering, data validation, model deployment, and monitoring.
• Manage and Build other Enterprise ETL using tools like ODI , informatica etc.

Big Data Platform Engineering

• Develop scalable data-processing workflows using Spark, Hadoop, Kafka, Airflow, Flink or similar.
• Optimize large datasets (transactional, bureau, behavioural, logs) for modelling in batch and real-time environments.
• Manage distributed computation and ensure reliability and fault tolerance.

Feature Store & Data Assets Management

• Design and maintain a centralized feature store for credit, fraud, marketing, and customer analytics models.
• Ensure feature consistency between training and serving environments.
• Implement versioning, lineage, documentation, and metadata management for data features.

Model Deployment & MLOps

• Collaborate with data scientists to deploy models using MLflow, Docker, Kubernetes, API gateways, CI/CD pipelines.
• Develop automated monitoring pipelines for model performance, drift detection, data quality, and explainability.
• Ensure models operate efficiently in real-time decision engines and batch scoring environments.

Data Quality & Governance

• Implement robust data validation, profiling, anomaly detection, and reconciliation checks.
• Work with Data Governance teams to ensure compliance with IFRS9, Basel, CBK, GDPR, and internal data standards.
• Manage data lineage, cataloguing, and documentation to support audits and regulatory reviews.

Collaboration & Stakeholder Support

• Partner with Data Scientists, Risk, Credit, Fraud, Marketing, and BI teams to align data pipelines with business use cases.
• Work with IT and Infrastructure teams on cluster performance, security, access controls, and SLA adherence.
• Participate in sprint planning, architecture reviews, and model implementation committee sessions.

Performance Optimization

• Improve the efficiency, scalability, and cost of ML workloads.
• Optimize database queries, Spark jobs, Kafka streams, and storage systems.

Qualifications & Experience:

• Strong academic foundation with a Bachelor’s or Master’s in Computer Science, Data Engineering, Data Science, Information Technology, or a related quantitative field.
• 3–7+ years of impactful, hands‑on experience in data engineering, big‑data processing, or building scalable ML infrastructure—ideally within fast‑paced, data‑driven environments.
• Advanced programming capability, with strong proficiency in Python, SQL, and PySpark; experience with Scala is an added advantage.
• Demonstrated expertise in modern data and ML platforms, including:
  • Big‑data technologies: Spark, Hadoop, Kafka, Airflow
  • MLOps & containerization: MLflow, Docker, Kubernetes
  • CI/CD pipelines: GitLab, Jenkins, GitHub Actions
  • Cloud platforms: AWS, GCP, or Azure (highly preferred)
• Experience working with banking systems, risk data, or credit‑modelling datasets—a significant advantage that accelerates success in this role.

Key Competencies

• Strong understanding of data structures, distributed systems, and ML workflows.
• Excellent problem-solving, debugging, and optimization skills.
• Fast learner with ability to adapt to new technologies.
• High attention to detail, documentation discipline, and data governance awareness.
• Strong collaboration and communication skills.