HSBC

3 weeks ago

Manager - DAAI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 10 hours agoStatus: Live

Job Summary

Lead analytics and AI delivery for Wealth MI by building dashboards, performance packs, and self-serve reporting that track client and commercial outcomes. Translate business problems into statistical and ML solutions, partnering with data and engineering teams to improve data quality and create reusable analytical datasets. Own end-to-end model lifecycle, support scalable Wealth platforms across markets, and apply responsible GenAI practices from prototyping to monitoring.

Location: Guangzhou

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Manager level

Key Responsibilities

•Deliver Wealth MI and insights through dashboards, performance packs, and self-serve reporting for key commercial and client outcomes.
•Convert business problems into analytical solutions by defining problem statements, success metrics, and hypotheses, then applying statistical/ML techniques (e.g., propensity, segmentation, forecasting, optimisation, scenario simulation).
•Partner with data and engineering teams to shape data requirements, improve data quality, and build reusable analytical datasets/pipelines for scalable Wealth analytics across markets.
•Own end-to-end model lifecycle, including feature design, validation, documentation, governance reviews, deployment handover, and ongoing monitoring to keep models robust.
•Contribute to large-scale Wealth platforms by defining analytics and measurement frameworks and supporting scalable, consistent, reusable analytics solutions across markets.

Key Requirements

•Strong technical foundations in SQL and Python for data wrangling, analysis, and automation (e.g., pandas/numpy; writing clear, efficient queries).
•Core analytics and ML knowledge, including statistics basics, supervised/unsupervised learning, feature engineering, and model evaluation (e.g., AUC, precision/recall) for Wealth use cases.
•Familiarity with GenAI concepts and delivery, including LLM capabilities/limitations, prompt engineering basics, and patterns like RAG, summarisation, classification, and agent workflows.
•Full-stack delivery mindset—comfortable working end-to-end from data extraction/transformation through basic API integration and front-end visualisation (dashboards/light UIs).
•Good engineering hygiene (e.g., Git/version control) and clear communication in English and Chinese with stakeholders across business, data engineering, and technology teams.

Experience:BankingFinancial servicesAnalyticsMachine learningArtificial intelligenceGenAIResponsible AI

Skills:CommunicationStakeholder managementProblem-solvingCollaborationLearning agility

Languages:EnglishChinese

Tech Stack:SQLPythonPandasNumpyGitAPIsLLMsRAGDashboardsMachine learning

Company Brief

HSBC

Global banking and financial services organisation offering retail, commercial, corporate and investment banking, wealth management, and global markets services across Europe, Asia, the Americas and the Middle East.

Industry: Banking

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: London, United Kingdom

Founded: 1865

Glassdoor: 3.6

Website LinkedIn

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HSBC

3 weeks ago

Manager - DAAI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 10 hours agoStatus: Live

Job Summary

Location: Guangzhou

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Manager level

Key Responsibilities

•Deliver Wealth MI and insights through dashboards, performance packs, and self-serve reporting for key commercial and client outcomes.
•Convert business problems into analytical solutions by defining problem statements, success metrics, and hypotheses, then applying statistical/ML techniques (e.g., propensity, segmentation, forecasting, optimisation, scenario simulation).
•Partner with data and engineering teams to shape data requirements, improve data quality, and build reusable analytical datasets/pipelines for scalable Wealth analytics across markets.
•Own end-to-end model lifecycle, including feature design, validation, documentation, governance reviews, deployment handover, and ongoing monitoring to keep models robust.
•Contribute to large-scale Wealth platforms by defining analytics and measurement frameworks and supporting scalable, consistent, reusable analytics solutions across markets.

Key Requirements

•Strong technical foundations in SQL and Python for data wrangling, analysis, and automation (e.g., pandas/numpy; writing clear, efficient queries).
•Core analytics and ML knowledge, including statistics basics, supervised/unsupervised learning, feature engineering, and model evaluation (e.g., AUC, precision/recall) for Wealth use cases.
•Familiarity with GenAI concepts and delivery, including LLM capabilities/limitations, prompt engineering basics, and patterns like RAG, summarisation, classification, and agent workflows.
•Full-stack delivery mindset—comfortable working end-to-end from data extraction/transformation through basic API integration and front-end visualisation (dashboards/light UIs).
•Good engineering hygiene (e.g., Git/version control) and clear communication in English and Chinese with stakeholders across business, data engineering, and technology teams.

Experience:BankingFinancial servicesAnalyticsMachine learningArtificial intelligenceGenAIResponsible AI

Skills:CommunicationStakeholder managementProblem-solvingCollaborationLearning agility

Languages:EnglishChinese

Tech Stack:SQLPythonPandasNumpyGitAPIsLLMsRAGDashboardsMachine learning

Company Brief

HSBC

Industry: Banking

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: London, United Kingdom

Founded: 1865

Glassdoor: 3.6

Website LinkedIn