Senior Applied Scientist

We'reASOS, the online retailer for fashion lovers all around the world.

We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too. At ASOS,you'refree to be your true self without judgement, and channel your creativity into a platform used by millions.

Everyone needs some help showing up as their best self.We'reDisability Confident Committed - let our Talent team know if you need any reasonable adjustments throughout the recruitment process

Job Description

We're looking for a Senior Applied Scientist to join our AI Demand Forecasting team. Our mission is to build forecasting capabilities that power critical business decisions across the company.

While our foundations are in replenishment forecasting, we're evolving into a forecasting platform that provides scalable, high-quality demand forecasts for a growing range of use cases, including AI-powered Pricing and Supply Chain Optimisation. This means solving complex machine learning challenges while developing reusable forecasting capabilities that can be applied across multiple business domains.

As a Senior Applied Scientist, you'll help shape the scientific direction of our forecasting platform, leading the design of advanced machine learning solutions that combine statistical modelling, deep learning and multimodal AI to solve challenging forecasting problems. You'll work closely with ML Engineers, data engineers, analysts, product managers and business stakeholders to translate research into production systems and create measurable business impact.

Responsibilities

• Lead and collaborate on the design, development and evaluation of machine learning models for demand forecasting and related AI products.
• Shape and support improvements in forecasting accuracy, robustness, scalability and explainability across multiple business domains.
• Develop reusable modelling approaches that support a growing forecasting platform serving replenishment, pricing, supply chain optimisation and future AI products.
• Research, prototype and evaluate modern and emerging machine learning techniques from academia and industry, identifying opportunities to improve forecasting performance.
• Design rigorous offline and online evaluation methodologies to measure both model quality and business impact.
• Write, test and maintain production-quality Python code for machine learning models and forecasting pipelines, following software engineering best practices to support reliable deployment and long-term maintainability.
• Work closely with ML Engineers to productionise models, ensuring scientific approaches can operate reliably and efficiently at scale.
• Provide technical leadership and guidance on complex modelling projects, helping shape scientific direction and investment decisions.
• Mentor and support other scientists through technical guidance, code reviews, experimentation best practices and knowledge sharing.
• Communicate scientific findings and recommendations clearly to both technical and non-technical stakeholders.

Qualifications

You'll be excited by solving complex machine learning challenges, advancing forecasting capabilities and seeing innovative research translated into measurable business impact.

We're interested in people with a range of experiences and backgrounds. If your experience doesn't align perfectly with every qualification below, we'd still encourage you to apply if you believe you'd be successful in the role.

You'll likely bring experience in some of the following areas:

• Developing and deploying machine learning models in production environments.
• Applying statistical methods to solve real-world problems.
• Experience in one or more of the following areas:
• Time series forecasting
• Probabilistic forecasting
• Deep learning
• Multimodal machine learning
• Representation learning
• Causal inference
• Optimisation
• Designing new modelling approaches or adapting modern machine learning research to practical business challenges.
• Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow or similar technologies.
• Experience working with large datasets and distributed data processing environments.
• Applying software engineering practices including testing, version control and developing maintainable, reproducible code.
• Collaborating with ML Engineers and cross-functional teams to deploy machine learning solutions into production.
• Communicating complex scientific concepts clearly to technical and non-technical audiences.
• Supporting and mentoring colleagues or contributing technical leadership across projects.
• Curiosity, pragmatism and sound judgement when balancing innovation with business outcomes.

Additional Information

• Employee sample sales
• 25 days paid annual leave + an extra celebration day for a special moment
• Discretionary bonus scheme
• Private medical care scheme
• Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits
• Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role

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Job Location

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