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# Senior Data Scientist (ML)

- **Company:** HSBC Group
- **Location:** London, United Kingdom
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Automation of Tests, Databases, Continuous Integration, DevOps, Python (Programming Language), PostgreSQL, Machine Learning, Microsoft SQL Server, Release Management, Tensorflow, Software Deployment, Software Engineering, Data Logging, Pytorch, Flask (Web Framework), Model Validation, Fastapi, Build Management, AI Platforms, Git Flow, Machine Learning Operations, Api Design, Software Version Control, Data Pipelines, Docker, Web Api
- **Published:** November 11, 2026
- **Apply:** https://dejobs.org/x/x/265E08A665034121B09ABCA4DCDED381/job/

## About the Role

* Bring strong software engineering experience delivering end-to-end services in production with ownership for run and support considerations
 * Demonstrate proficiency in Python and modern engineering practices including clean code testing packaging dependency management and Git-based workflows
 * Apply hands-on experience with AI deployment patterns and infrastructure such as Docker Kubernetes API-based serving and batch or stream inference
 * Use practical MLOps experience including CI/CD for ML model packaging release management automated validation monitoring and lifecycle management
 * Work with ML and DL frameworks and tooling such as PyTorch or TensorFlow and the Python ML ecosystem to implement training adjacent evaluation and inference pipelines
 * Evaluate model performance on large messy and imbalanced datasets and explain trade-offs in metrics and approach to stakeholders
 * Build and deploy web APIs using libraries such as Flask or FastAPI
 * Use database technologies such as SQL Server or Postgres
 * Explain technical designs risks and operational considerations clearly to wide-ranging audiences
 * Manage delivery across multiple initiatives through prioritisation and time management

## Description

* Design build deploy and operate production-grade AI services (ML and or GenAI) that are secure scalable and reusable
 * Productionise PoC and PoV work into hardened solutions with clear performance resilience cost and security requirements and defined service ownership
 * Build and maintain MLOps and LLMOps pipelines including CI/CD automated testing packaging promotion and rollback and model and version management
 * Develop and implement model evaluation approaches for complex and imbalanced datasets including defining metrics validation methods and performance trade-offs
 * Implement observability for AI services including logging metrics tracing model performance monitoring and quality and drift checks with actionable alerting
 * Build inference pipelines using the Python ML ecosystem and ML or DL frameworks such as PyTorch or TensorFlow
 * Partner with data scientists and data engineers to integrate data pipelines feature inputs and evaluation results into production services
 * Translate business requirements into technical and modelling designs and communicate trade-offs and recommendations clearly to technical and non-technical stakeholders