Data Science Engineer, Assistant Senior Manager at Diamond Trust Bank (DTB)
Data Science Engineer, Assistant Senior Manager
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.
Data Science Engineer, Credit Scoring, ML & Advanced Analytics, Assistant Senior Manager 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
Senior Manager, Data Governance, Data Analytics and AI Department at Diamond Trust Bank (DTB)
Job Purpose
The Senior Manager Data Governance will lead the bank’s
enterprise-wide data governance strategy, ensuring strong oversight of data
quality, metadata, privacy, regulatory compliance, and data lifecycle
management. The role manages the entire governance team and serves as a key
advisor to the CDO and senior leadership.
Key Roles & Responsibilities
Strategic Leadership
- Develop
and implement the enterprise data governance strategy.
- Oversee
governance operating model and roadmap.
- Align
governance initiatives with architecture and digital programs.
Governance Oversight & Team Leadership
- Lead
the data governance team.
- Define
KPIs and performance targets.
- Mentor
governance managers and technical leads.
Policy, Standards & Framework Management
- Own
enterprise data governance policies and standards.
- Implement
compliance reviews and audits.
Enterprise Data Quality Management
- Oversee
Critical Data Elements (CDE) program.
- Ensure
remediation plans and data quality dashboards.
Metadata, Lineage & Data Catalog Implementation
- Govern
metadata standards and lineage.
- Ensure
accurate data flow documentation.
Regulatory Compliance & Risk Management
- Ensure
compliance with CBK, GDPR/DP Act, Basel, CRB, AML/KYC.
- Represent
governance in internal and external audits.
Data Governance Council & Stakeholder Management
- Chair
Data Governance Council.
- Produce
governance reports for executives.
Data Lifecycle & Controls Assurance
- Oversee
data lifecycle controls.
- Ensure
compliance with system changes and product rollouts.
Culture, Change Management & Data Literacy
- Lead
data literacy initiatives.
- Promote
a data-driven culture across the bank.
Qualifications & Experience
Minimum Qualifications
- Bachelor’s
degree in ICT, Data Management, Computer Science, Statistics, or related.
- Master’s
degree preferred.
- Certifications
such as DAMA/CDMP/DCAM preferred.
Experience Requirements
- 10+
years in data governance or data management.
- 5+
years leading teams.
- Mandatory
financial services experience.
- Experience
with data governance tools.
Key Skills & Competencies
- Leadership
and team development.
- Knowledge
of DAMA/DCAM frameworks.
- Strong
banking regulatory knowledge.
- Expertise
in metadata, DQ, MDM, and privacy.
- Strong
communication and stakeholder engagement.
Graduate Management Trainee Program at Diamond Trust Bank (DTB)
Title: Graduate Management Trainee Program
Diamond Trust Bank (DTB) invites ambitious graduates to
shape their future through its Graduate Management Trainee Program.
This program offers hands-on experience, mentorship, and leadership
opportunities for young professionals eager to drive innovation and build a
successful career in banking.
- Location: Nairobi,
Kenya
- Application
Deadline: November 30, 2025
Who Should Apply:
- Fresh
graduate from a recognised university
- Must
have attained Second Class Honours and above
- Below
25 years of age
Why Join DTB:
- Gain
practical exposure in banking operations
- Access
mentorship from experienced professionals
- Develop
leadership skills for future roles
How to Apply
Interested candidates meeting the above requirements are
encouraged to apply online via DTB’s official career portal:
Future Leaders Begin at DTB – Shape Your Tomorrow!
Manager, Information Systems Security Audit, Internal Audit at Diamond Trust Bank (DTB)
Role Purpose:
The Manager, Information Systems (IS) Security Audit, is a
key member of the Internal Audit Team tasked with the responsibility of
performing cyber security information systems audits throughout the Bank’s IS
Infrastructure Systems and Business Applications including audits of complex
computer applications and technological solutions in accordance
with the existing IS Workplan and professional standards on IS auditing,
Internal Audit Methodology, processes, procedures and timeframes.
Key Responsibilities:
Under the direct supervision of the Head Information Systems
Audit or designate, the incumbent will be expected to, inter alia: –
- Perform
IT security audits across the Bank’s IT infrastructure, including DTB
Group.
- Carry
out Vulnerability Assessment and Penetration Testing (VAPT) across the DTB
Group.
- Review
digital products, software development and related systems/Applications/
Application Programming Interfaces – APIs/ software.
- Assess
the Software/System development life cycle and management methodology/
approach and Security benchmarks.
- Review
cloud deployments infrastructures and security posture.
- Participate
in any IT-related investigations as and when required.
Academic & Professional Qualifications:
- BSc.
in Information Technology/ Computer Science or other IT related Degree.
- Certificate
in Cybersecurity OSCP/C-PENT or other VAPT courses.
- Certificate
in IT Audit CISA/ CISM/ CEH/ CHFI/ CISSP.
- Certificate
in cloud engineering.
Relevant Experience
- 5
years’ practical work experience in VAPT and software development,
preferably in financial services industry.
- Good
understanding of programming languages such as Python, Java, JavaScript,
React, Node JS, etc.
- Good
understanding of various Databases such as Oracle, SQL, Cassandra, Mongo
DB, Postgress etc.