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# Data Scientist

- **Company:** Harnham
- **Location:** Cardiff, UK
- **Experience:** Experienced
- **Salary:** £120,000.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Continuous Integration, Python (Programming Language), Linux System Administration, Machine Learning, NumPy, Tensorflow, Data Ingestion, Pytorch, Git, Pandas, Scikit Learn, Kubernetes, Data Management, Machine Learning Operations, Hardware Infrastructure, Docker, Databricks
- **Published:** November 2, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/data-scientist/54702982

## About the Role

3+ years of experience in Data Science.
Strong proficiency in Python and experience with ML libraries/frameworks (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving).
Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and feature/data platforms (Databricks, Tecton, etc.).
Strong experience with cloud platforms (AWS, GCP, or Azure), Docker, and Kubernetes.
Solid coding practices, including Git, automated testing, and CI/CD.
Proficiency with Linux environments.
Knowledge of time-series analysis is a strong plus.

## Description

We are working with a leading hedge fund and alternative investment firm, specialising in global macro strategies with a particular emphasis on inflation modelling and the wider economic landscape., They are looking for a Data Scientist to join their London-based team, working closely with AI and research functions to design, implement, and maintain scalable ML models for forecasting. This role offers the opportunity to work at the cutting edge of time-series forecasting within a fast-paced and intellectually rigorous environment., Building machine learning models, with a focus on time-series forecasting.
Collaborating with data scientists and researchers to productionise models.
Managing both cloud-based and on-premise infrastructure.
Ensuring reliability and scalability of ML systems in production.