Data Scientist (Machine Learning)

Location: London or Manchester, United Kingdom; hybrid with 1–2 office days per week

Company

hirify.global is a product company operating a high-volume digital e-commerce business.

What you will do

• Develop and evaluate machine learning models for recommendations, personalisation, customer modelling, and predictive modelling.
• Explore data, engineer features, compare modelling approaches, and select practical solutions.
• Design and analyse A/B tests and other experiments, connecting model performance with customer and commercial outcomes.
• Partner with Product, Engineering, MLOps, Commercial, and Marketing to translate business problems into practical data science solutions.
• Integrate models into production ML pipelines and contribute to deployment, monitoring, data-quality analysis, and ongoing improvement.
• Write tested, modular, maintainable Python and SQL; participate in code reviews, documentation, and reusable tooling.

Requirements

• Experience developing machine learning or advanced analytical solutions in a Data Science, Machine Learning, or Advanced Analytics role.
• Strong practical knowledge of supervised machine learning, feature engineering, validation, overfitting, and model evaluation.
• Strong Python and SQL skills, with experience applying them to real-world data and modelling problems.
• Experience designing or analysing A/B tests, selecting success metrics, and interpreting results.
• Experience with Git or similar version-control tools, testing, reproducibility, and maintainable software-development practices.
• Understanding of production ML workflows, including deployment, monitoring, data quality, privacy, fairness, security, and model lifecycle considerations.

Nice to have

• Experience in B2C e-commerce, retail, or a high-volume digital environment.
• Experience with recommendation or personalisation systems, propensity, uplift, or customer lifetime value modelling.
• Experience applying LLMs, embeddings, or other generative AI capabilities to product or data science problems.
• Experience with AWS, dbt, or other cloud-based data and analytics engineering tooling.
• Degree in Statistics, Mathematics, Economics, Computer Science, or a related quantitative discipline; equivalent practical experience is accepted.

Culture & Benefits

• Permanent employment with a hybrid working model.
• Collaborative work across Data Science, Product, Engineering, MLOps, Commercial, and Marketing.
• Emphasis on high-quality, reproducible code, thoughtful experimentation, knowledge sharing, and reusable tools.
• Use of AI-assisted development tools for coding, analysis, experimentation, and documentation with critical validation of outputs.
• Competitive salary and benefits.

Hiring process

• Initial recruiter screening.
• Hiring Manager interview, Technical Screening, Technical Interview follow-up, and Final Round.
• The exact process may change, with candidates informed of updates.