Ingest, clean and transform internal and external risk datasets
Develop predictive models using machine learning and statistical methods
Build and maintain reliable data pipelines
Build dashboards and visualisations for senior management
Maintain data lineage, quality checks and reconciliations
Ingest, clean and transform datasets from internal systems
Develop and implement predictive models using machine learning, statistical methods and natural language processing
Build and maintain reliable data pipelines to support consistent and repeatable analysis
Develop dashboards and visualisations using Power BI, Tableau, Python or equivalent tools
Maintain data integrity through data lineage documentation, quality checks and reconciliation processes
5–8 years of experience in data science, advanced analytics or statistical modelling
Python / R / SQL
Statistical Modelling and Machine Learning
Risk Management and Regulatory Knowledge
Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science or a related discipline
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.
Qualifications • 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. About Standard Chartered
From the employer’s posting.
Our strategy, which combines cross-border capabilities and leading wealth management expertise, helps us deliver our purpose - to drive commerce and prosperity through our unique diversity.
In the employer’s words · Read in context
Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.
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Day 5
Early. Similar jobs usually come down between day 8 and day 60.
At Standard Chartered Bank, jobs usually come down by about day 24.
From 11,873 similar postings that came down, and 140 of Standard Chartered Bank’s. Coming down is not always a hire.
35 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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Key Responsibilities 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.
Skills and Experience 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 Qualifications
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New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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.
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.
We're a global bank connecting clients to our differentiated network, offering growth opportunities in the world's most dynamic markets. Our strategy, which combines cross-border capabilities and leading wealth management expertise, helps us deliver our purpose - to drive commerce and prosperity through our unique diversity.
Over our 170-year history, and across a unique geographical footprint connecting Asia, Africa and the Middle East to each other and the world, we've built a bank like no other, with diverse capabilities and partnerships that set us apart.
Visit our careers website to learn more about us: www.sc.com/careers.
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London, GB
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