Top Tech Jobs & Startup Jobs in London

Own profitable e-commerce trading performance across assigned markets. Analyze revenue, traffic, conversion, margin, discounts, customer behavior, and market trends to recommend commercial actions. Lead forecasting, scenario planning, performance reviews, campaign evaluation, and test-and-learn strategies. Partner with pricing, marketing, finance, product, technology, and digital trading teams to deliver measurable outcomes, improve decision-making through AI and automation, and support P&L management.

Designs, builds, and operates scalable machine learning platforms, infrastructure, deployment frameworks, and backend services for search, recommendations, and personalization. Responsibilities include CI/CD, batch and real-time inference, observability, infrastructure automation, distributed compute optimization, incident management, architecture, and technical mentoring. The role partners with data scientists, software engineers, data engineers, and product managers to deliver reliable cloud-native ML systems at scale.

Design, build, deploy, and operate large-scale batch and real-time machine learning systems for search, ranking, recommendations, and personalization. Collaborate with applied scientists, software engineers, and product managers to deliver measurable customer impact. Improve engineering practices, contribute to technical direction, and mentor other engineers across production machine learning, deep learning, distributed training, GPU infrastructure, and MLOps.

Leads product analytics for ASOS, owning KPIs, customer funnel analysis, experimentation, A/B testing, dashboards, and data-driven product strategy. Partners with Product Managers and senior stakeholders to identify growth opportunities, improve conversion, engagement, and retention, and shape roadmaps. The role also mentors junior analysts and promotes rigorous, insight-led decision-making across product teams.

Build and evolve Azure-based data platform capabilities supporting data science, machine learning, and analytics at scale. Develop high-performance pipelines with Python, Scala, Spark, and Databricks; improve reliability, observability, data quality, and cost efficiency; create reusable engineering tools and patterns; contribute to architecture and technical standards; and mentor engineers across teams.

Lead the architecture and production delivery of large-scale machine learning systems for personalized recommendations, search relevance, AI Stylist experiences, and fashion discovery. Set technical direction across recommendation, retrieval, deep learning, LLM, and MLOps initiatives; translate research into reliable customer-facing products; mentor senior engineers; influence multiple teams; and drive engineering standards, scalability, observability, and responsible AI adoption.

Build and operate production machine learning systems supporting customer engagement, marketing, pricing, personalization, and commercial decision-making. Own data ingestion, feature engineering, deployment, monitoring, and optimization across the ML lifecycle. Develop reusable MLOps tooling and infrastructure, influence architecture, partner with Applied Scientists and Data Engineers, and mentor engineers while delivering scalable solutions with measurable customer and business impact.

Activates ASOS’s employer brand and EVP across the employee lifecycle through campaigns, content, communications, recruitment marketing, employee advocacy, and careers-site optimization. Partners with People Experience, Internal Communications, Talent, Brand, Creative, agencies, and business stakeholders. Develops toolkits and activation materials, analyzes campaign performance, manages external partners, and supports inclusive employer-branding initiatives, budget tracking, and stakeholder engagement.

Leads ASOS’s SEO and GEO strategy across technical SEO, content, digital PR, and LLM discovery. Owns organic growth, search visibility, measurement, roadmaps, and SEO requirements for site architecture, crawlability, indexation, and product development. Manages technical and content SEO teams, agencies, and senior stakeholder relationships while partnering with Product, Engineering, Marketing, UX, Editorial, and Commercial teams across international markets.

Build and scale shared AI platform capabilities on Azure, with emphasis on agentic AI runtimes, orchestration, tool integration, LLMOps, AgentOps, governance, observability, and reliability. Develop reusable templates and reference implementations, secure model and enterprise-tool access, support lifecycle evaluation and CI/CD practices, and establish cost-aware, production-ready patterns for AI agents. Partner with product, cloud infrastructure, security, and engineering teams to enable safe, scalable enterprise AI adoption.