Job description

Our purpose – Opening up a world of opportunity – explains why we exist. Here at HSBC, we use our unique expertise, capabilities, breadth, and perspectives to open up new kinds of opportunity for our more than 40 million customers. We’re bringing together the people, ideas and capital that nurture progress and growth, helping to create a better world – for our customers, our people, our investors, our communities, and the planet we all share. In compliance with applicable laws, HSBC is committed to employing only those who are authorized to work in the US. Applicants must be legally authorized to work in the U.S. as HSBC will not engage in immigration sponsorship for this position. CBA (Client & Banking Analytics) transforms data into strategic analytics that improve commercial performance across HSBC CIB (Corporate and Institutional Banking). We support better decisions on where to invest time and resources across clients, products, and channels, using consistent methodology and strong governance. As an Associate in Client Intelligence, you’ll help deliver advanced analytics (including machine learning/predictive analytics/timeseries models) that commercialize HSBC’s internal client data and integrate external datasets to generate actionable insights. You’ll own defined analytics workstreams end-to-end (from problem framing to delivery), partnering closely with stakeholders to identify growth opportunities, define high-potential clients, and optimize customer value to guide investment decisions across clients, products, and channels. Where appropriate, you’ll use approved AI tools, including emerging agentic workflows and AI-driven analytics, to accelerate delivery, with appropriate validation and governance. We are looking for individuals who are not only analytically strong, but also curious about how emerging AI capabilities can enhance analytical workflows, productivity, and insights generation. As our Associate, Client Intelligence you will: • Analytics Delivery: Extract, clean, and join datasets (internal and external) using SQL and Python; perform exploratory analysis and basic feature engineering; follow machine learning/predictive modelling lifecycle to build, test, and deploy analytical models for client profiling, segmentation, wallet sizing, customer value measurement, and growth use cases. Use approved AI tools, including agentic workflows, to support analysis and automation where relevant. • Technical Expertise: Understand and make trade-offs based on dataset analysis and model performance in build/validation phases, perform hyper-parameter tuning, examine multiple accuracy measures, use relevant libraries to explain feature importance /contribution to model output and post deployment model performance monitoring. • Insight Generation & Storytelling: Translate the findings and analysis into clear recommendations, commercial implications, and practical next steps for frontline and CIB leadership. • Stakeholder Partnership: Work with coverage, product, and servicing teams to understand needs, refine requirements, and support adoption of analytics outputs into business routines. • Data & Quality Management: Partner with data engineering/technology to access data, improve data quality, document assumptions, and follow governance standards. • Operational Excellence: Create and maintain robust model documentation, support the model governance process, performance monitoring, and repeatable processes; contribute to reusable code and standard methodologies. • Collaboration & Coaching: Support junior colleagues through peer review, knowledge sharing, and best-practice guidance (without necessarily being a formal people manager). You´ll likely have the following qualifications to succeed in this role: • Bachelor’s degree required; Master’s preferred in a quantitative field (Data Science, Statistics, Computer Science, Mathematics, Economics, or similar). • Strong experience in data science/machine learning, ideally within financial services or a commercial analytics environment. • Working knowledge of Python (preferred), SQL; experience with modelling techniques (predictive, statistical, ML). Familiarity with big data and/or cloud environments (e.g., Spark/Hadoop; AWS/Azure/GCP) and visualization tools (Power BI/Tableau). • Experience with disciplined coding practices and have a systems mindset • Familiarity with AI-assisted analytics and a practical approach to when to use it and how to validate outputs. • Working knowledge of CIB client segments and commercial drivers; experience in at least some of: client segmentation, wallet sizing, client profiling, peer benchmarking, pipeline/sales analytics. • Curious, structured problem-solver; able to spot patterns, test hypotheses, and communicate findings clearly • Able to explain methods and results to non-technical audiences; comfortable presenting insights to stakeholders and iterating based on feedback. • Comfortable operating in a global matrix; organized, delivery-focused, and able to manage multiple priorities. As an HSBC employee, you will have access to tailored professional development opportunities to ensure you have the right skills for today and tomorrow. We offer a competitive pay and benefits package including a robust Wellness Hub, all in a welcoming and inclusive work environment. You will be empowered to drive HSBC’s engagement with the communities we serve through an industry-leading volunteerism policy, a generous matching gift program, and a comprehensive program of immersive Sustainability and Climate Change Initiatives. You’ll want to join our Employee Resource Groups as they play a central part in life at HSBC, including the development of our employees and networking inside and outside of HSBC. We value difference. We succeed together. We take responsibility. We get it done. And we want you to help us build the bank of the future! Your final fixed pay offer will depend on the candidate and several variables, including but not limited to, role responsibilities, skill set, depth of experience and education, licensing/certification requirements, internal relativity, and specific work location. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, race, national origin, ethnicity, disability or medical condition, genetic information, military or veteran service, religion, creed, sex, gender, pregnancy, childbirth, caregiver status, marital status, citizenship or immigration status, sexual orientation, gender identity or expression or any other trait protected by applicable law.

Requirements

Must-haves

We are looking for individuals who are not only analytically strong, but also curious about how emerging AI capabilities can enhance analytical workflows, productivity, and insights generation.
Insight Generation & Storytelling: Translate the findings and analysis into clear recommendations, commercial implications, and practical next steps for frontline and CIB leadership.
Stakeholder Partnership: Work with coverage, product, and servicing teams to understand needs, refine requirements, and support adoption of analytics outputs into business routines.
Data & Quality Management: Partner with data engineering/technology to access data, improve data quality, document assumptions, and follow governance standards.
Operational Excellence: Create and maintain robust model documentation, support the model governance process, performance monitoring, and repeatable processes; contribute to reusable code and standard methodologies.
Collaboration & Coaching: Support junior colleagues through peer review, knowledge sharing, and best-practice guidance (without necessarily being a formal people manager).
You´ll likely have the following qualifications to succeed in this role:
Strong experience in data science/machine learning, ideally within financial services or a commercial analytics environment.
Experience with disciplined coding practices and have a systems mindset
Familiarity with AI-assisted analytics and a practical approach to when to use it and how to validate outputs.
Working knowledge of CIB client segments and commercial drivers; experience in at least some of: client segmentation, wallet sizing, client profiling, peer benchmarking, pipeline/sales analytics.
Curious, structured problem-solver; able to spot patterns, test hypotheses, and communicate findings clearly
Able to explain methods and results to non-technical audiences; comfortable presenting insights to stakeholders and iterating based on feedback.
Comfortable operating in a global matrix; organized, delivery-focused, and able to manage multiple priorities.

Nice-to-haves

Bachelor’s degree required; Master’s preferred in a quantitative field (Data Science, Statistics, Computer Science, Mathematics, Economics, or similar).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaisqlpythonmlawsazuregcp

Platform: eightfold
Effort: medium

Benefits

Benefits detected in the listing.

Wellness

Professional Development