Top Tech Jobs & Startup Jobs in London

Analyze customer behavior and travel product performance using large datasets. Translate complex findings into actionable recommendations, dashboards, reports, and presentations for technical and non-technical stakeholders. Collaborate with Product, Design, Data Science, and external partners; help define success metrics and support evidence-based decision-making across travel and disruption initiatives.

Own the UK brand strategy and lead integrated campaigns from brief through launch. Translate global strategy into local execution, guide creative agency partners, support product launches, establish campaign measurement and optimization, manage brand tracking and budget, and coach junior team members. The role requires strong consumer brand marketing experience, creative judgment, commercial acumen, stakeholder influence, and comfort using data to drive decisions.

Drive revenue for Trainline Media by acquiring and growing media agency and direct client accounts. Own client relationships, develop data-driven advertising proposals, collaborate on campaign delivery and optimization, manage sales pipelines, negotiate contracts, meet commercial targets, and provide performance insights demonstrating ROI. The role requires strong digital advertising knowledge, relationship-building, presentation, negotiation, and CRM skills.

Support the design, development, evaluation, and delivery of machine learning models and AI solutions. Responsibilities include data exploration, feature engineering, model selection, stakeholder collaboration, and contributing to ML tools, frameworks, workflows, and production systems. The role involves working cross-functionally with data scientists, software engineers, data engineers, and product managers while developing skills across the machine learning lifecycle.

Own investigations and data-driven solutions across search, supply, and commerce. Analyze complex datasets using SQL and Python, identify incident patterns and product opportunities, connect data sources, and support cross-functional customer experience initiatives. Mentor junior colleagues, guide problem-solving, balance competing priorities, and help convert recurring issues into lasting improvements. Marketplace or product analytics experience is beneficial but not essential.

Supports People Partners and mid-level managers with employee relations, organisational design, restructures, performance management, capability development, and change initiatives. Collaborates with ER, Learning and Development, Talent Acquisition, Reward, People Operations, and Legal teams. Improves People processes, workflows, and manager development pathways while exploring AI and automation to enhance efficiency and employee experience.

Leads end-to-end development of Trainline’s consumer brand propositions, especially for new products and markets. Uses customer insight, research, behavioral science, data, and competitor analysis to shape hypotheses and define compelling solutions. Partners with Product, UX, Engineering, Brand, Creative, Growth, and regional teams to launch propositions, establish execution guidelines, define success metrics, and report business and marketing impact to senior stakeholders.

Lead discovery, scoping and delivery of new market and carrier launches across Europe. Own product strategy, prioritize integrations, define success metrics, partner with Engineering, Commercial, Legal, Finance and GTM, and embed AI to accelerate delivery while ensuring commercial and technical feasibility.

Provides Tier 2 workplace technology support across Windows and macOS environments. Responsibilities include incident and service request resolution, ticket management, asset lifecycle administration, employee onboarding and offboarding, AV/VC support, technical troubleshooting, documentation, security compliance, and collaboration with Tier 3 teams. The role is onsite in London and supports internal employees through a Tech Bar, ITSM platform, remote tools, and internal events.

Build scalable data pipelines, data models, feature stores, and cloud-native data applications supporting machine learning workloads. Partner with Machine Learning Engineers and Data Scientists to create reliable datasets, maintain production pipeline quality and observability, and support batch and real-time data systems. The role uses Python, SQL, Spark, Airflow, AWS, CI/CD, and modern data engineering technologies.