Manager , Data and Analytics

In this role, you will: 

• Research, design and develop innovative machine learning and deep learning algorithms to solve complex problems across financial services and other relevant operational domains. Conduct analytical experiments systematically, compare alternative approaches and select solutions that meet agreed business objectives and performance criteria.
• Perform end-to-end data analysis, including data extraction, cleaning, validation, exploration and error analysis, using large-scale data stores. Apply appropriate algorithms and models to identify meaningful patterns, improve predictive accuracy and ensure that analytical results are reliable, explainable and fit for purpose.

• Maintain, enhance and productionise existing machine learning and deep learning models in line with stakeholder-agreed success criteria. Monitor model performance, data quality, stability and business outcomes after deployment, and implement recalibration or improvement activities when results fall below the required standard.

• Define and continuously improve feature engineering processes to ensure that models use relevant, robust and compliant features while reflecting business constraints. Deliver complete analytical solutions using Structured Query Language, NoSQL databases and Python, covering data preparation, modelling, validation, deployment, monitoring and documentation.

• Complete analytical and strategy projects to agreed timelines and quality standards, document methodologies and results, and present clear findings and recommendations to stakeholders. Identify new opportunities and lead self-initiated projects that generate measurable business benefits across the customer lifecycle.

• Use prompt engineering and retrieval-augmented generation to develop responsible large language model solutions that provide useful, traceable and actionable outputs. Work effectively within an agile and Scrum delivery framework, identify and close process gaps proactively, promote innovation across the team and maintain high standards for models, analyses, insights, documentation and stakeholder outcomes.

To be successful you will:

• Bring 2-5 years of practical experience building enterprise-level data science solutions, supported by a master’s degree in technology, a master’s degree or a doctorate in computer science, mathematics, statistics or another relevant quantitative discipline. Apply specialist knowledge of machine learning, deep learning and natural language processing to deliver reliable solutions for complex business problems.

• Demonstrate excellent Python and Structured Query Language skills, including the ability to write production-grade scripts, optimise code and review implementations for quality, scalability, maintainability and performance. Use sound software engineering practices, version control through Git and appropriate controls for production environments.

• Apply strong foundations in mathematics, probability, statistics and algorithms, together with practical knowledge of supervised learning, adversarial learning and unsupervised learning. Use deep analytical thinking and structured problem-solving skills to select, develop, validate and improve analytical approaches.

• Use Structured Query Language, Machine Learning, Generative Artificial engineering, Deep learning, BigQuery and Python scripting to extract, transform, analyse and validate data. Apply advanced spreadsheet skills, including Microsoft Excel and Visual Basic for Applications where appropriate, to support detailed analysis, automation and quality assurance.

• Design, develop and support artificial intelligence products, including large language models and small language models, with experience in training, fine-tuning and quantisation. Apply classical artificial intelligence, generative artificial intelligence, retrieval-augmented generation, prompt engineering and LangChain to create responsible, scalable and business-focused solutions.

• Use Google Cloud Platform native tools, including BigQuery and Google Cloud Storage, to develop and operate data science solutions at enterprise scale. A Google Cloud Platform certification in Professional Data Engineer or Google Cloud Architect would be advantageous, alongside a strong understanding of cloud security, data governance and production deployment practices.

You'll achieve more at HSBC

 Hsbc.com/careers

HSBC is an equal opportunity employer committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and, opportunities to grow within an inclusive and diverse environment. We encourage applications from all suitably qualified persons irrespective of, but not limited to, their gender or genetic information, sexual orientation, ethnicity, religion, social status, medical care leave requirements, political affiliation, people with disabilities, color, national origin, veteran status, etc., We consider all applications based on merit and suitability to the role.”

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