The posting
Allegis Global Solutions is the exclusive Contingent HR services provider for one of the world’s leading banks.
At Allegis Global Solutions we’re proud to be the leader in global talent solutions. We draw upon decades of industry expertise to develop innovative tools, products, processes and strategies focused on outcome.
Moreover, we’re incredibly proud to have built a culture that empowers our people to make their mark while making deep connections that will last a lifetime. With our passion and culture for talent, we are truly transforming the way the World acquires talent.
Our Client is one of the world's most international banks with over 1,100 branches, offices and outlets in 67 countries. They operate in some of the world's most dynamic markets and have been for over 150 years. More than 90 per cent of their income and profits are derived from Asia, Africa and the Middle East. Their brand promise, Here for good, underlines their distinctive approach.
RESPONSIBILITIES
The Operational, Technology and Cyber Risk (OTCR) organisation is instrumental in protecting and ensuring the resilience of the Bank’s operations, data, and IT systems by managing operational, technology and cyber risk across the enterprise. As acritical function reporting into the Group Chief Risk Officer (CRO), the Group OTCR team serves as the second line of defence for assuring that controls are implemented effectively, in accordance with the OTCR Framework, and for instilling a risk culture within the Bank.
The Data Scientist will utilize advanced analytics and data science techniques to support the Operational Technology & Cyber Risk (OTCR) Transformation Programme. This role is responsible for transforming complex internal and external data into actionable insights that enhance risk management, strengthen decision-making, and support transformation objectives. By working closely with stakeholders across Risk, Technology, and the COO, the Data Scientist will integrate modelling, data management, and effective communication to enable evidence-based decisions and foster a data-driven culture within OTCR.
Role Specific Responsibilities:
1. Data Analysis & Modelling
- Perform extensive exploratory data analysis to identify trends, anomalies, and insights from diverse datasets.
- Develop and implement predictive models, machine learning algorithms, statistical methods, and natural language processing (NLP) techniques as appropriate.
- Apply quantitative analysis to support scenario assessments, thematic reviews, and stress testing.
- Ensure that model outputs are explainable, validated, and compliant with internal governance standards.
2. Data Management & Quality Assurance
- Ingest,clean, and transform datasets from various internal systems (e.g., M7, iTrack,FICA) and external sources (e.g., ORX, industry loss databases).
- Buildand maintain pipelines to ensure reliable and repeatable data flows.
- Assure data integrity by documenting data lineage, applying quality checks, and implementing reconciliation processes.
- Maintain datasets that are audit-ready and capable of withstanding regulatory or internal scrutiny.
3. Visualisation & Communication
- Develop clear and insightful dashboards and visualizations using tools such as Power BI, Tableau, or Python libraries.
- Present analytical findings in a manner that allows senior stakeholders to quickly grasp key insights.
- Communicate technical results clearly and structurally, ensuring consistency and clarity across governance and committee materials.
4. Collaboration & Stakeholder Support
- Collaborate with risk subject matter experts (SMEs), technology teams, and business partners to frame analytical problems and define practical solutions.
- Provide data-driven insights that enable decision-making rather than just reporting.
- Support cross-functional teams by sharing analytical expertise and fostering a culture of evidence-based decision-making.
- Contribute to enhancing data literacy and analytical capability within the OTCR function.
QUALIFICATIONS & SKILLS:
- Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science or a related field.
- 5–8 years of experience in data science or advanced analytics, preferably in financial institutions or risk management.
- Solid foundation in data analysis, statistical modelling, and machine learning.
- Practical experience with data cleaning, pipeline development, and quality assurance.
- Proven experience in supporting large-scale transformation or regulatory programmes.
- Proficiency in data visualization tools, with the ability to present technical outputs in a simplified manner.
- Strong analytical and critical thinking skills, with a keen attention to detail.
- Familiarity with risk frameworks, operational risk data, and regulatory environments is desirable.
- Excellent communication skills, capable of simplifying and translating complex analyses into actionable insights.
- Collaborative mindset with the ability to work across global teams and influence decision-making.
Common Tools Used:
- Python / R / SQL: Data science, modelling, and automation.
- Spark / SAS (advantageous): Large-scale data processing.
- Power BI / Tableau: Data visualization.
- Microsoft Excel/ Access: Analysis and reconciliation.
- SharePoint / MS Teams: Document management and collaboration.
