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AVP Data Modeling and Anaytics

HSBC
New, NY Full Time
POSTED ON 12/5/2025 CLOSED ON 1/4/2026

What are the responsibilities and job description for the AVP Data Modeling and Anaytics position at HSBC?

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.


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.


Analytics, Digital and Architecture (ADA) is a recently established function within Global Finance. ADA’s aim is to be the pioneering force shaping the future of analytics, engineering, and innovation to empower insight and strategic decision making in Finance. ADA is a globally diverse team. The ADA Analytics team aims to ensure that Finance develops models, methodologies and other analytics that bring tangible benefit to the users, housed within a robust framework and compliant with regulatory rules.


As our AVP, Modeling and Analytics you will:

  • Manage the model life-cycle, while engaging with business and finance teams
  • Ensure that the models continue to be fit for purpose via establishing an effective model validation framework
  • Maintain documentation and control for models in line with internal policies and regulatory guidance
  • Onboard and maintain models on internal model execution platforms
  • Working with business partners to provide financial and technical analysis and recommendations regarding current and/or proposed PPNR models
  • Working with businesses, risk and finance teams to submit and validate data and analyze the consolidated results
  • Development/enhancement of models, methodologies and infrastructure across Finance predominantly related to forecasting, stress testing

You´ll likely have the following qualifications to succeed in this role:

Minimum qualifications:

  • Advanced (masters or PHD equivalent) degree in a quantitative (e.g. science, mathematics, engineering, econometric, financial engineering) field.
  • Experience in developing timeseries, regression models
  • Proficiency in Python with experience using libraries like NumPy, Polars, Scikit-learn, TensorFlow and PyTorch
  • Strong ability for problem solving and attention to detail
  • Strong communication, analytical, and presentation skills

Good to have:

  • 5 years of relevant work experience
  • Knowledge of bank stress testing and CCAR requirements
  • Experience building machine learning models
  • Commercial acumen – good knowledge of Banking products and its dynamics, conceptual soundness of related financial ratios, P&L and drivers
  • Ability to operate in a fast-paced environment and with all levels of internal and external management and government regulators


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!

Salary : $104,000 - $510,600

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