> Markdown version of [/jobs/ext/4710882-director-otcr-data-scientist](https://www.wearedevelopers.com/jobs/ext/4710882-director-otcr-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, OTCR, Data Scientist - **Company:** Standard Chartered - **Location:** Epsom, United Kingdom - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Data Analysis, Big Data, Databases, Data Governance, Data Visualization, Python (Programming Language), Machine Learning, Natural Language Processing, Power BI, SAS (Software), Tableau (Software), Apache Spark, Model Validation, Information Technology, Data Lineage, Data Management, Data Pipelines - **Published:** November 14, 2026 - **Apply:** https://www.apply4u.co.uk/jobs/director-otcr-data-scientist/56437458 ## About the Role Priority Skills Data Science and Advanced Analytics Statistical Modelling and Machine Learning Data Management and Quality Assurance Python / R / SQL Data Visualisation and Reporting Analytical and Critical Thinking Risk Management and Regulatory Knowledge Stakeholder Engagement and Communication Data Governance and Model Validation Transformation Programme Support Other Skills Spark / SAS and Large-Scale Data Processing Power BI / Tableau Data Pipeline Development Scenario Analysis and Stress Testing Cross-Functional Collaboration, 5-8 years of experience in data science, advanced analytics or statistical modelling, preferably within financial services or risk management. Practical experience in data analysis, machine learning, data pipeline development and data visualisation, with the ability to communicate technical findings to non-technical stakeholders. Experience supporting large-scale transformation or regulatory programmes is desirable. Bachelor's or Master's degree in Data Science, Statistics, Computer Science or a related discipline. Relevant professional certifications in data science, analytics or related disciplines are desirable. Proficiency in English, including the ability to communicate technical findings clearly to business stakeholders. ## Description Data Analysis and Modelling Perform exploratory data analysis to identify trends, anomalies and insights across internal and external datasets. Develop and implement predictive models using machine learning, statistical methods and natural language processing, where appropriate. Apply quantitative analysis to support scenario assessments, thematic reviews and stress testing. Ensure model outputs are explainable, validated and compliant with internal governance standards. Data Management and Quality Assurance Ingest, clean and transform datasets from internal systems (e.g. M7, iTrack, FICA) and external sources (e.g. ORX and industry loss databases). Build and maintain reliable data pipelines to support consistent and repeatable analysis. Maintain data integrity through data lineage documentation, quality checks and reconciliation processes. Ensure datasets are audit-ready and meet internal governance and regulatory requirements. Data Visualisation and Communication Develop dashboards and visualisations using Power BI, Tableau, Python or equivalent tools. Present analytical findings clearly to support senior management decision-making. Translate complex technical outputs into practical insights for governance and committee reporting. Stakeholder Collaboration and Analytical Support Work closely with Risk SMEs, Technology teams and business stakeholders to define analytical requirements and develop practical solutions. Provide data-driven insights to support risk management and transformation decisions. Share analytical expertise and contribute to building data literacy and analytical capabilities within OTCR. Risk Governance and Compliance Ensure analytical activities comply with applicable risk management frameworks, data governance standards and regulatory requirements. Maintain appropriate documentation, controls and evidence to support model validation, audit and regulatory scrutiny. Escalate material data quality issues, analytical limitations and risks to relevant stakeholders.