Careers at ASOS

At ASOS, we're building the next generation of AI-powered Search and Discovery experiences for millions of customers worldwide. Our Search & Recommendations team develops the platforms, infrastructure and machine learning systems that power personalised product discovery, ranking, retrieval and recommendation experiences across ASOS. We operate high-scale production systems that enable data scientists and ML engineers to rapidly develop, deploy and monitor machine learning solutions in a reliable, scalable and cost-effective way. We're looking for a Senior Machine Learning Engineer - with strong Engineering experience - who enjoys solving complex engineering challenges at scale. This role is ideal for someone with a strong software engineering/MLOps, distributed systems or platform engineering background who wants to work at the intersection of machine learning and production infrastructure. As a Senior Engineer, you'll be responsible for designing, building and operating the platforms and services that enable machine learning models to be trained, deployed and served reliably across ASOS. You'll work closely with Applied Scientists, Software Engineers, Data Engineers and Product Managers to create the tooling, infrastructure and deployment frameworks that power recommendation systems, search relevance, personalisation and emerging AI applications. This is a highly engineering-focused role with an emphasis on cloud-native systems, platform architecture, automation, observability and operational excellence. What You'll Be Doing: Design and build scalable machine learning platforms and infrastructure supporting model training, deployment and serving. Develop highly available backend services that power recommendation, search and personalisation experiences for millions of customers. Build and maintain CI/CD pipelines for machine learning and data products. Design batch and real-time inference architectures using modern cloud-native technologies. Improve reliability, resilience and performance across ML workloads through monitoring, observability and automation. Build tooling and frameworks that enable data scientists and ML engineers to deploy models safely and efficiently. Own production services, infrastructure and operational excellence practices, including incident management and root cause analysis. Optimise distributed compute workloads and resource utilisation across cloud environments. Drive Infrastructure-as-Code adoption and platform standardisation across machine learning systems. Contribute to architectural decisions across recommendation, search and AI platforms. Mentor engineers and promote software engineering best practices across the organisation. Help shape ASOS's long-term machine learning platform strategy. We're interested in candidates who bring experience across modern software engineering, distributed systems and machine learning infrastructure. We recognise that expertise can be developed through a variety of backgrounds, including Backend Engineering, Platform Engineering, Site Reliability Engineering (SRE), Cloud Engineering, MLOps or Machine Learning Engineering. We'd love to see experience in several of the following: Strong software engineering fundamentals with experience designing and building production systems at scale. Experience developing distributed systems, microservices or high-throughput backend platforms. Strong programming skills in Python, Java, Kotlin, Go, Scala or similar languages. Experience building and operating services in AWS, Azure or GCP environments. Hands-on experience with Kubernetes, containerisation and cloud-native technologies. Experience implementing CI/CD pipelines and automated deployment processes. Knowledge of Infrastructure-as-Code tools such as Terraform, Pulumi or CloudFormation. Experience with monitoring, alerting and observability tooling. Experience building reliable, resilient and scalable systems with a focus on performance and operational excellence. Experience working with data-intensive systems, streaming technologies or large-scale distributed processing platforms. Exposure to machine learning systems, model serving, feature stores, training infrastructure or MLOps practices. Experience supporting recommendation systems, search platforms, personalisation engines or other customer-facing data products is advantageous. Comfortable providing technical leadership, mentoring engineers and influencing architectural direction. Strong collaboration and communication skills, with experience working in cross-functional product teams. Experience supporting large-scale model training and inference workloads. Knowledge of vector search, ranking systems, retrieval architectures or recommendation platforms. Exposure to LLMs, Generative AI and production AI systems. Experience building internal developer platforms, engineering enablement tooling or shared capabilities used across multiple teams. BeneFITS’ Employee discount (hello ASOS discount!) 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

We're looking for a Senior Machine Learning Engineer to join our Search & Recommendations team, where we're building the machine learning systems that help millions of customers discover products every day. Sitting within ASOS's Search & Recommenders area, the team is responsible for the recommendation, ranking and personalisation systems that sit at the heart of the customer journey. From surfacing the most relevant products and outfits to powering personalised shopping experiences, our work directly influences how customers discover and shop fashion on ASOS. You'll work on large-scale machine learning systems that power experiences such as Similar Items, People Also Viewed and personalised customer journeys that adapt in real time. Leveraging signals from millions of customer interactions, we use recommendation systems, ranking models, deep learning and emerging AI technologies to connect customers with the products they're most likely to love. As a Senior Machine Learning Engineer, you'll play a key role in designing, building and operating production machine learning systems at scale. Working alongside Applied Scientists, Machine Learning Engineers, Software Engineers and Product Managers, you'll help turn innovative ideas into reliable, high-performing systems that deliver measurable customer and commercial impact. This is an opportunity to tackle challenging problems across recommendation systems, search, ranking, personalisation and deep learning, while helping shape the future of machine learning at ASOS. What you'll be doing: Work as part of a cross-functional team designing, building and improving machine learning systems that power search, ranking and recommendation experiences. Collaborate closely with Applied Scientists and engineers to develop and deploy machine learning solutions that deliver measurable customer and commercial value. Build, deploy and maintain batch and real-time machine learning models in production environments. Contribute to recommendation, ranking and personalisation capabilities that support millions of customer interactions each day. Continuously improve our systems, codebase and engineering practices while contributing ideas for new features and capabilities. Support and mentor other engineers through coaching, knowledge sharing and technical collaboration. Contribute to the team's technical direction and help evolve machine learning standards, best practices and ways of working across the wider ML community. About You We're interested in candidates who bring experience in several of the following areas. You'll likely be someone who enjoys combining strong software engineering fundamentals with machine learning expertise and is excited by the challenge of building reliable, scalable systems that deliver real-world impact. You may come from a recommendation systems, search, ranking, personalisation, deep learning or broader machine learning background. Most importantly, you'll enjoy solving complex technical problems, collaborating across disciplines and helping bring machine learning products from experimentation into production. Experience applying machine learning and deep learning techniques in production environments. Experience using deep learning frameworks and distributed computing technologies to build and deploy large-scale machine learning models. Experience working with distributed training infrastructure, GPU-based training environments and parallelisation approaches. Strong understanding of software engineering principles, development lifecycles and MLOps practices. Experience developing reliable, scalable machine learning systems in production. Comfortable providing technical leadership, mentoring and support to other engineers. Strong collaboration and communication skills, with the ability to work effectively across engineering, science and product teams. BeneFITS’ Employee discount (hello ASOS discount!) 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

Join the team responsible for powering the data and machine learning capabilities behind millions of customer experiences at ASOS. As part of the Data Science Platform group within Nishantha's organisation, you will help build and evolve the core data infrastructure that enables Data Scientists, ML Engineers and Analysts across Forecasting, Recommendations, Pricing, Marketing and Customer domains to develop, deploy and operate data-driven products at scale. This is an opportunity to work on a modern Azure-based data platform, solving large-scale data engineering challenges focused on scalability, reliability, performance and developer experience. Your work will directly support the delivery of machine learning and analytics capabilities across ASOS. What you’ll be doing Designing and building data platform capabilities that support data and machine learning workloads across ASOS. Developing high-performance data pipelines and processing frameworks using Python, Scala, Spark and Databricks. Owning and evolving platform components that help engineers and data scientists build, test, deploy and monitor data products. Improving platform reliability, observability, data quality and operational excellence. Creating reusable libraries, tooling and engineering patterns that enable teams to deliver data products more efficiently. Partnering with Data Scientists, ML Engineers and Product Engineering teams to solve data challenges and enable new machine learning use cases. Contributing to architectural decisions and the evolution of data engineering standards across the organisation. Optimising distributed workloads for performance, scalability and cost efficiency across the Azure ecosystem. Supporting the long-term development of ASOS's data platform and engineering practices. Working with multiple Data Science and Machine Learning teams across ASOS to deliver platform capabilities that create business value. We're interested in people who can demonstrate many of the following capabilities. If your experience does not match every requirement exactly, we still encourage you to apply. You are likely to have: Experience building or operating large-scale data platforms or data-intensive applications in a cloud environment. Experience working with Databricks, Spark and distributed data processing technologies. Experience developing production-grade data engineering solutions using Python and/or Scala. Experience designing data architectures that balance scalability, reliability and cost efficiency. Experience implementing modern engineering practices, including CI/CD, automated testing, observability and Infrastructure as Code. Demonstrated ability to solve complex engineering problems and improve platform capabilities that help other teams work more effectively. Experience leading the design and delivery of complex data engineering solutions and contributing to technical direction and engineering best practices. Experience mentoring engineers through technical guidance, code reviews and knowledge sharing. Ability to collaborate effectively across teams and stakeholders, balancing business priorities with technical excellence to deliver scalable, reliable and maintainable data solutions. BeneFITS’ Employee discount (hello ASOS discount!) 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

We're looking for a Principal Machine Learning Engineer to join our Search & Discovery team and help define the technical direction of AI-powered fashion discovery at ASOS. Our mission is to help millions of customers discover outfits that reflect their personal style, preferences and current fashion trends. As part of the Search & Discovery organisation, we bring together recommendation systems, personalisation, deep learning and large language model (LLM) technologies to create new ways for customers to explore fashion beyond traditional ecommerce experiences. As a Principal Machine Learning Engineer, you'll shape the technical architecture behind large-scale machine learning systems spanning personalized product & outfit recommendations, conversational agent experiences like AI Stylist, search relevance, style discovery and intelligent product experiences across the customer journey. You'll work closely with Machine Learning Scientists, Software Engineers, Engineering Leaders and Product Managers, providing technical leadership across Search & Discovery while remaining hands-on with architecture, system design and engineering decisions. This is a highly influential role where you'll help shape the future of machine learning engineering at ASOS while mentoring others and driving engineering excellence across the organisation. Technical Architecture & System Design Own the end-to-end technical architecture for machine learning systems powering personalised fashion experiences, including outfit discovery, homepage ranking and AI Stylist experiences. Lead the design and evolution of large-scale batch and real-time machine learning systems serving millions of customers. Drive cross-team architectural decisions to ensure consistency, scalability, reliability and maintainability. Establish long-term technical direction for recommendation, retrieval and AI-powered discovery platforms. ML Product Engineering & Production Delivery Set technical direction and best practices across recommendation systems, retrieval, personalisation, search relevance, deep learning and generative AI applications. Partner with Machine Learning Scientists and Engineering Leaders to translate research and experimentation into robust production systems. Identify and resolve architectural, scalability, reliability and performance challenges throughout the machine learning lifecycle. Support the delivery of production-grade customer-facing ML products that generate measurable business and customer value. Technical Leadership & Engineering Excellence Provide technical leadership on strategic initiatives, including platform investment decisions and build-versus-buy evaluations. Mentor and support Senior, Staff level engineers, helping develop engineering capability across the organisation. Promote engineering excellence, modern software engineering practices and responsible adoption of AI-assisted development tools. Contribute to technical standards, architectural principles and engineering best practices across multiple teams. Stakeholder Influence Drive the development of shared machine learning capabilities, tools and frameworks used across Search & Discovery and wider engineering teams. Represent Search & Discovery engineering in discussions with senior stakeholders, technology partners and business leaders. Communicate technical strategy, trade-offs and outcomes clearly to both technical and non-technical audiences. Experience We're interested in candidates with significant experience across a number of the following areas. We recognise that expertise can be developed through different career paths and experiences. Extensive experience designing, building and operating large-scale machine learning systems in production environments. Experience owning and influencing technical architecture across complex engineering ecosystems. A product-focused mindset with a passion for applying machine learning and AI to customer and business challenges. Extensive experience across the machine learning lifecycle, including data analysis, feature engineering, model development, evaluation, deployment, monitoring and continual improvement. Experience building scalable, observable and highly reliable machine learning services using cloud-based technologies, distributed infrastructure and large datasets. Technical expertise Deep expertise in two or more of the following areas: ranking and relevance, recommendation systems, deep learning, large language models, information retrieval, natural language processing or content understanding. Advanced hands-on experience with frameworks such as PyTorch, TensorFlow or similar machine learning frameworks. Strong programming skills in Python and/or other languages such as Java or C++. Deep understanding of MLOps practices, including deployment, observability, monitoring and lifecycle management at scale. Experience building production AI systems using approaches such as retrieval-augmented generation (RAG), agent-based architectures, retrieval systems, model evaluation frameworks and ML-driven scoring approaches. Significant experience using AI-assisted engineering tools and coding agents, such as Claude Code, Codex, Cursor or similar technologies, throughout the software development lifecycle. Leadership capabilities Ability to establish and communicate a compelling technical vision and influence multiple teams without direct management responsibility. Demonstrated experience setting architectural direction, driving engineering strategy and encouraging adoption of technical standards across teams. Experience mentoring senior engineers and supporting wider engineering development. Strong communication and stakeholder management skills, including engagement with senior technical and business leaders. BeneFITS’ Employee discount (hello ASOS discount!) 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

At ASOS, machine learning is a core part of how millions of customers discover products, engage with our brand and shop every day. We're looking for a Senior Machine Learning Engineer to join our Customer & Martech team. In this role, you'll help build and scale machine learning products that support customer growth, marketing effectiveness, pricing and personalisation. You will work with some of ASOS's richest datasets, including customer behaviour, transactions, marketing interactions and product data, turning these into production-grade machine learning systems that deliver measurable value for customers and the business. Working alongside Applied Scientists, Data Engineers and Machine Learning Engineers, you'll contribute across the full lifecycle of machine learning products, from ideation and experimentation through to deployment, monitoring and optimisation. Whether improving customer retention, optimising marketing investment, supporting intelligent pricing decisions or helping build the next generation of customer experiences, you'll work on complex challenges at significant scale. What you'll be doing: Design, build and operate machine learning systems that support customer engagement, marketing effectiveness, pricing and commercial decision-making. Own the end-to-end engineering lifecycle of machine learning products, including data ingestion, feature engineering, deployment, monitoring and optimisation. Productionise advanced machine learning solutions and ensure they operate reliably at ASOS scale. Partner closely with Applied Scientists to translate research and experimentation into scalable production systems. Help shape the future of our MLOps platform by contributing to engineering best practices, operational excellence and platform capabilities. Build reusable tooling, frameworks and infrastructure that accelerate machine learning delivery and reduce operational overhead. Influence technical direction and architectural decisions across machine learning products and platforms. Mentor colleagues and support high standards of engineering quality, reliability and scalability. This is an opportunity to work on machine learning products used by millions of customers, leveraging rich datasets across customer behaviour, marketing, pricing and ecommerce. You'll collaborate with Applied Scientists, Machine Learning Engineers and Data Engineers to solve complex challenges at the intersection of machine learning, software engineering and large-scale data systems, while seeing the direct impact of your work on customer experience and commercial outcomes. You'll also help shape the future of ASOS's machine learning platform and engineering standards in an environment where machine learning is a core business capability. We recognise that people may not meet every requirement listed above. If your experience is relevant to the role and you believe you could contribute to the team, we encourage you to apply. Experience building, deploying and operating machine learning systems in production environments. Strong software engineering fundamentals, including expertise in Python and modern engineering practices. Experience building scalable batch and real-time machine learning pipelines in cloud environments. Strong understanding of MLOps principles, including model deployment, monitoring, CI/CD, observability and operational excellence. Experience working with large-scale data processing technologies such as Spark. Strong understanding of machine learning frameworks such as PyTorch, TensorFlow, XGBoost or similar technologies. Experience designing reliable APIs, services and platforms that support machine-learning-powered products. Ability to work through ambiguity and lead complex technical initiatives. Experience in customer intelligence, marketing optimisation, pricing, forecasting or personalisation. Experience building feature platforms, ML platforms or shared machine learning infrastructure. Exposure to experimentation frameworks, causal inference or measurement platforms. Experience mentoring engineers and influencing technical direction beyond your immediate team. A track record of delivering machine learning solutions that generated measurable customer or commercial outcomes. What's in it for you? Employee discount (hello ASOS discount!) 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

As a Senior AI Engineer, you will be part of the AI Platform team, helping to build and scale the shared foundations that enable AI capabilities across ASOS. The primary focus of this role will be contributing to the Agentic AI Platform initiative, alongside other core AI platform capabilities as the platform evolves. This role is focused on the platform layer, rather than individual business use cases. You will design and implement shared standards, templates and reference implementations for agentic AI on Azure, enabling application teams to safely design, deploy and operate AI agents at enterprise scale. Working closely with Product teams, Cloud Infrastructure, Security and partners, you will help ensure AI capabilities are secure, observable, reusable and governed by default. You will also contribute to the production foundations needed to operate AI capabilities reliably, including LLMOps, model access patterns, prompt and agent lifecycle practices, observability and secure enterprise integration. What you’ll be doing Designing and building AI platform capabilities on Azure, with a strong focus on agentic AI patterns such as agent runtimes, orchestration and tool integration Contributing to the Agentic AI Platform initiative, helping define how agents are built, integrated and operated across the organisation Designing and maintaining standardised templates and reference implementations for LLM and Generative AI workflows, enabling teams to adopt consistent patterns for prompt design, tool calling, multi‑step agent flows, retries and failure handling Implementing secure, governed access patterns for LLMs and enterprise tools using APIM, platform gateways, Entra ID, RBAC and managed identities Contributing to LLMOps and model runtime patterns, including standard approaches for model access, routing, caching, token optimisation and cost‑aware usage controls Supporting lifecycle and evaluation practices for agent configurations, prompts and AI workflows, including testing, controlled change and release readiness Designing secure tool-access patterns for agents, including MCP/tool abstraction, credential management and enterprise API integration. Contributing to AgentOps and GenAIOps capabilities, including telemetry, run history, task outcomes, error analysis and feedback loops Contributing to reliability patterns for production AI systems, including latency monitoring, alerting, scaling considerations and operational readiness. Applying CI/CD and software engineering best practices to AI platform and agentic components Embedding observability by default, ensuring AI systems are measurable, debuggable and auditable through logs, metrics and traces Partnering with Cloud Infrastructure and Security teams to design secure, scalable and cost‑effective Azure environments Significant experience as an AI Engineer, AI Platform Engineer or similar, delivering production‑grade AI systems Hands‑on experience with LLMs, Generative AI and agent‑based systems in real‑world environments Strong understanding of the end‑to‑end AI lifecycle, from experimentation through deployment and operation Practical understanding of production LLM or GenAI runtime concerns, such as model access, routing, caching, token usage, cost optimisation and reliability. High proficiency in Python, with experience building APIs and service‑oriented systems Experience working with CI/CD pipelines, automated testing and versioned deployments for AI or platform components Practical experience with observability tooling (logging, metrics, tracing and alerting) and using telemetry to improve reliability and performance Comfortable working in cloud environments, preferably Azure Experience with Azure AI Foundry is preferred, but we are equally open to candidates with hands‑on experience using comparable GenAI or agent platforms, and a strong understanding of how to apply those patterns within Azure Experience with Azure API Management (APIM) is preferred, especially as a governance or integration boundary Familiarity with AgentOps, MLOps or GenAIOps concepts, including monitoring, evaluation and feedback loops Strong collaboration skills, with the ability to influence platform standards and enable other engineering teams A pragmatic, engineering‑led approach to responsible and ethical AI, with a focus on safety, reliability and trust BeneFITS’ Employee discount (hello ASOS discount!) 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

We're looking for an iOS Engineer to join our Customer Experience team and help build the mobile experiences that millions of customers use every day. At ASOS, you'll work on one of the UK's most recognisable retail apps, helping over 8 million active customers discover products, stay inspired and shop seamlessly from anywhere. Our iPhone and iPad apps maintain excellent App Store ratings and are built using modern iOS technologies, including the latest versions of Swift. You'll join a collaborative team of Engineers, Product Managers and Designers, working together to solve real customer problems and deliver experiences at scale. This is a great opportunity to grow your technical expertise, contribute to products with genuine business impact, and help shape the future of mobile commerce at ASOS. What you'll be doing Building and delivering high-quality features that enhance the experience of millions of customers worldwide. Developing scalable, reliable and well-tested applications using modern iOS technologies and engineering best practices. Collaborating closely with Product Managers, Designers and Engineers to take ideas from concept through to launch. Working within agile, cross-functional teams to solve customer, business and technical challenges. Taking ownership of your work, contributing ideas and helping improve our products, processes and engineering practices. Supporting the quality, performance, accessibility and maintainability of our mobile applications. Contributing to technical discussions and helping evolve our iOS platform as we continue to scale. Learning from experienced engineers while sharing knowledge and collaborating across the wider engineering community. Why join us? Build products used by millions of customers around the world. Work with modern iOS technologies, including the latest versions of Swift. Be part of a collaborative engineering culture that values quality, innovation and continuous improvement. Develop your skills through complex technical challenges, mentorship and exposure to large-scale systems. Have a direct impact on the experiences that shape how customers discover and shop fashion online. Join a team where your ideas are valued and your contributions make a visible difference. About you Experience building iOS applications using Swift and the core iOS frameworks Comfortable working with Xcode, Interface Builder and Auto Layout to create responsive user interfaces Understanding of Apple’s Human Interface Guidelines and platform design principles Knowledge of common software design patterns and architecture concepts An interest in writing clear, maintainable code and improving existing codebases Experience building apps that work well across iPhone and iPad screen sizes Familiarity with unit testing and a willingness to contribute to test coverage Experience with UI automation tools, or a desire to learn them BeneFITS’ Employee discount (hello ASOS discount!) 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

We're looking for a Machine Learning Engineer to join our Search & Recommendations team , where we build the machine learning systems that help millions of customers discover products every day. From personalised recommendations and product ranking to emerging AI-powered styling experiences, our work sits at the heart of the customer journey and directly influences how customers explore and shop on ASOS. Our recommendation and ranking systems power experiences such as Similar Items , People Also Viewed , and personalised customer journeys that adapt in real time based on customer behaviour. These systems operate at significant scale, using signals from millions of interactions to surface the most relevant products and content. As a Machine Learning Engineer, you'll work across the full machine learning lifecycle – from experimentation and model development through to deployment, monitoring and optimisation in production environments. You'll collaborate closely with Machine Learning Engineers, Applied Scientists, Software Engineers and Product partners to transform ideas into reliable, scalable systems that deliver measurable customer and commercial impact. You'll also help shape the future of discovery at ASOS, contributing to areas such as next-generation recommendation systems, sequence-based modelling, outfit generation and AI-driven styling experiences. What you'll be doing Designing, building and maintaining production-grade machine learning systems that power personalisation and product discovery Developing and improving recommender systems, ranking models and customer-facing machine learning capabilities Deploying models into batch and real-time environments, ensuring reliability, scalability and performance at scale Collaborating with Applied Scientists and Engineers to take models from experimentation into robust production systems Monitoring, evaluating and iterating on models using real-world customer behaviour and performance metrics Contributing to engineering best practices, MLOps tooling and shared machine learning platform capabilities Helping to improve how machine learning is developed, deployed and operated across the organisation About You We're keen to hear from Machine Learning Engineers who enjoy solving real-world problems, learning from others and building systems that deliver meaningful impact. You don't need to meet every requirement below to apply. If this role sounds exciting and aligns with your experience or career ambitions, we'd love to hear from you. Experience developing, deploying or operating machine learning solutions in production environments Familiarity with modern machine learning frameworks and tooling such as PyTorch, TensorFlow, XGBoost or similar technologies Experience training models using GPUs, or an interest in distributed computing and scalable machine learning systems Understanding of software engineering fundamentals, including version control, CI/CD, testing, observability and containerisation An appreciation of MLOps practices and the challenges of deploying machine learning systems at scale Strong collaboration and communication skills, with experience working across engineering, science and product disciplines Curiosity, adaptability and a genuine enthusiasm for learning new technologies and approaches BeneFITS’ Employee discount (hello ASOS discount!) Employee sample sales 25 days paid annual leave + an extra celebration day for a special moment Private medical care scheme Fixed Annual Payment in addition to your salary each year, it's just an extra thank you from us Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role

What You’ll Be Doing Behavioural Data Modelling Build and extend core behavioural models in Databricks that describe how customers interact with ASOS across web and app Design and maintain: Session logic Funnels and journeys Attribution logic Feature usage and engagement metrics Experiment exposure and variant datasets Create domain specific behavioural marts optimised for analytics and experimentation use cases Web Analytics Data Pipeline Ownership Own the quality and consistency of behavioural events flowing into Analytics platforms Ensure events conform to agreed: Schemas and naming conventions Data types and required fields Privacy first compliance Build and maintain transformation pipelines where enrichment or standardisation is required Act as a technical owner of event contracts between frontend teams and analytics Data Quality & Observability In collaboration with the teams software engineers implement end-to-end data quality checks across frontend → ingestion → Analytics → Databricks Monitor and alert on: Schema changes and validation failures Event completeness and coverage Cardinality drift Volume anomalies Identity and user stitching integrity Proactively identify and resolve issues before they impact experiments or reporting Semantic Layer Enablement Enable trusted behavioural metrics through: Databricks metric enabled views Power BI semantic models Ensure metrics are usable for: Self serve analysis Executive and leadership reporting “Talk to Data” and agent based workflows Partner with product analysts, data and product teams to ensure metrics are clear, consistent, and reusable Frontend Instrumentation Alignment Work closely with web and app engineers to ensure instrumentation meets analytics and experimentation needs Support: Event payload and schema design Instrumentation PR reviews Pre‑release validation Experiment tagging and exposure tracking Act as a go to expert for behavioural tracking best practices We’re Looking For Core Skills & Experience Experience in analytics engineering, data engineering, or product analytics Strong SQL and experience working in Databricks / Spark / DBT/ Python Solid understanding of behavioural and event based data modelling Hands‑on experience with product analytics platforms (e.g. Mixpanel, Adobe or similar) Experience building reliable data pipelines and quality controls Comfortable working closely with software engineers within product teams on data instrumentation A pragmatic, detail oriented approach to data quality Nice to Have Experience supporting experimentation and A/B testing Knowledge of identity resolution and cross device tracking Power BI semantic modelling experience Experience enabling self serve analytics Interest in AI assisted analytics or metric driven agents Employee discount (hello ASOS discount!) Employee sample sales 25 days paid annual leave + an extra celebration day for a special moment Private medical care scheme Fixed Annual Payment in addition to your salary each year, it's just an extra thank you from us Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role

We're looking for a Senior Applied Scientist to join the team – whose mission is to build machine learning capabilities that power critical business decisions, products and customer experiences across ASOS. You'll work on complex, high-impact machine learning challenges, developing scalable solutions that can be applied across a range of business domains. As we continue to expand our AI capabilities, you'll play a key role in shaping scientific approaches, identifying new opportunities for machine learning, and translating research into practical solutions that deliver measurable value. As a Senior Applied Scientist, you'll provide technical leadership across initiatives, partnering closely with ML Engineers, Data Engineers, Analysts, Product Managers and business stakeholders to design, develop and deploy machine learning solutions at scale. You'll help shape both our scientific direction and the machine learning capabilities that underpin our products and decision-making. Responsibilities Lead the design, development and evaluation of machine learning solutions for complex business challenges. Identify opportunities where machine learning can create measurable value. Drive improvements in model performance, scalability, reliability and operational impact across a range of use cases. Research, evaluate and prototype emerging approaches from industry and academia, identifying opportunities to enhance existing capabilities. Design robust evaluation frameworks to assess model quality, customer outcomes and business impact. Write, test and maintain production-quality code, applying software engineering best practices to support scalable and maintainable solutions. Partner closely with ML Engineers and Data Engineers to ensure solutions can be deployed and operated effectively at scale. Provide technical leadership on complex initiatives, influencing scientific direction and technical decision-making. Mentor and support other scientists through coaching, code reviews, knowledge sharing and technical guidance. Communicate complex technical concepts and recommendations clearly to both technical and non-technical stakeholders. You'll likely bring experience in some of the following areas: Developing and deploying machine learning solutions in production environments. Applying machine learning techniques to solve complex real-world problems. Leading the design and evaluation of data-driven solutions that deliver measurable business value. Developing new approaches or adapting research and emerging technologies to practical business challenges. Working across the end-to-end machine learning lifecycle, from problem definition and experimentation through to deployment and monitoring. Proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow or similar technologies. Experience working with large datasets and distributed data processing environments. Applying software engineering best practices including testing, version control and developing maintainable, reproducible code. Collaborating effectively with engineers, product teams and business stakeholders to deliver production-ready solutions. Communicating complex technical concepts clearly to technical and non-technical audiences. Providing technical leadership, mentoring others and influencing scientific direction across projects. Curiosity, pragmatism and sound judgement when balancing innovation with business outcomes. BeneFITS’ Employee discount (hello ASOS discount!) 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