Senior Machine Learning Scientist

HSBC; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

HSBC is seeking a Senior Machine Learning Scientist to design, build, deploy, and operate production-grade AI services that are secure, scalable, and reusable across Wholesale use cases. You will shape end-to-end ML deployments and contribute to resilient, observable systems in a global banking environment.

You will collaborate with data scientists, platform teams, and governance stakeholders to ensure robust technical designs, clear trade-offs, and compliant implementations that uplift

Qualifications

• Proven experience delivering end-to-end production ML services.
• Strong Python software engineering practices and testing.
• Hands-on ML deployment, MLflow or similar tools, and CI/CD for ML.
• Collaboration with data scientists and governance/risk stakeholders.

Responsibilities

• Design, build, deploy, and operate production‑grade AI services for Wholesale use cases.
• Productionise PoC/PoV work into hardened solutions with performance and security requirements.
• Build and maintain MLOps pipelines for repeatable releases.
• Develop reusable assets to reduce technical debt and accelerate delivery.
• Implement observability and model performance monitoring with drift checks.
• Partner with data scientists and platform teams to meet control and documentation standards.
• Translate business requirements into technical designs and communicate trade‑offs clearly.

Skills

Python programming

ML deployment

MLOps

Kubernetes

Docker

Flask/FastAPI

SQL/NoSQL databases

Git-based workflows

Communication skills

Education

Master's degree in a relevant field

Tools

Docker

Kubernetes

Flask

FastAPI

PyTorch

TensorFlow

Job description

If you’re looking for a career that will help you stand out, join HSBC, and fulfil your potential - whether you want a career that could take you to the top, or an exciting new direction, we offer opportunities, support and rewards that will take you further.

We’re one of the largest banking and financial services organisations in the world, with a network that covers more than 50 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people fulfil their hopes and realise their ambitions.

We are seeking a Senior Machine Learning Scientist

You’ll play a key part in designing, building, deploying, and operating production‑grade AI services (ML and/or GenAI) that are secure, scalable, and reusable across Wholesale use cases

In this role you’ll:

• Design, build, deploy, and operate production‑grade AI services (ML and/or GenAI) that are secure, scalable, and reusable across Wholesale use cases
• Productionise PoC/PoV work into hardened solutions with clear non‑functional requirements (performance, resilience, cost, security) and defined service ownership
• Build and maintain MLOps/LLMOps pipelines (CI/CD, automated testing, packaging, promotion/rollback, model/version management) to enable repeatable releases
• Develop reusable engineering assets (libraries, templates, reference architectures, infrastructure‑as‑code patterns) to reduce technical debt and accelerate delivery
• Implement observability for AI services (logging/metrics/tracing), model performance monitoring, and quality/drift checks with actionable alerting
• Partner with data scientists, data engineers, platform teams, and governance/risk stakeholders to ensure end‑to‑end delivery meets control, auditability, and documentation expectations
• Translate business requirements into technical designs; communicate trade‑offs and recommendations clearly to both technical and non‑technical stakeholders
• Contribute to engineering standards and ways of working (code reviews, design reviews, documentation) and help uplift team capability through practical coaching

To be successful in this role you should meet the following requirements:

• Strong software engineering experience delivering end‑to‑end services in production (not just notebooks/experiments), with ownership for run/support considerations
• Proficiency in Python and modern engineering practices (clean code, testing, packaging, dependency management, Git‑based workflows)
• Hands‑on experience with AI deployment patterns and infrastructure (e.g. containerisation with Docker, orchestration such as Kubernetes, API‑based serving, batch/stream inference)
• Practical MLOps experience: CI/CD for ML, model packaging and release management, automated validation, monitoring, and lifecycle management
• Working knowledge of ML/DL frameworks and tooling (e.g. PyTorch/TensorFlow and the Python ML ecosystem) sufficient to collaborate effectively with data scientists and implement inference pipelines
• Experience working with complex, multi‑layered datasets (including imbalanced data) and integrating data pipelines into AI services
• Hands‑on experience building and deploying web APIs using libraries such as Flask or FastAPI.
• Proficiency with database technologies such as SQL Server or Postgres, etc.
• Strong stakeholder communication skills: able to explain technical designs, risks, and operational considerations to wide‑ranging audiences
• Good organisational skills and delivery discipline (prioritisation, time management, working across multiple initiatives)
• Degree in a relevant field or equivalent practical experience (Masters highly preferred)
• Model explainability approaches/tools (e.g. SHAP) where required by use case and governance expectations (desirable)

Being open to different points of view is important for our business and the communities we serve. At HSBC, we’re dedicated to creating diverse and inclusive workplaces - no matter their gender, ethnicity, disability, religion, sexual orientation, socio‑economic background or age. We are committed to removing barriers and ensuring careers at HSBC are inclusive and accessible for everyone to be at their best. We take pride in being a Disability Confident Leader and will offer an interview to people with disabilities, long‑term conditions or neurodivergent candidates who meet the minimum criteria for the role.