Top Tech Jobs & Startup Jobs

Perform product and behavioural analysis (funnel, cohort, segmentation), define KPIs, run A/B tests, build dashboards, partner with Product and Analytics Engineering, and apply AI/ML techniques to drive product decisions and experimentation.

Lead design and evolution of the product operating model, implement AI-enabled workflows and tooling, define operational standards across product lifecycle, improve processes and knowledge management, drive adoption of modern product practices, and partner cross-functionally to enable faster, insight-driven product delivery.

Design, implement, test, secure, and maintain backend services for commercial journeys. Collaborate with product and cross-functional engineering teams, embed testing and security standards, contribute to CI/CD pipelines, and participate in engineering communities and agile delivery practices.

Lead high‑impact AI and decisioning projects from discovery through production. Define technical roadmaps, design and validate models (including causal/experimentation), partner with engineering to productionise services with monitoring and governance, mentor data scientists, and promote responsible AI practices and platform adoption.

Design, build and maintain scalable automated test frameworks and reusable test solutions. Create API, integration, end-to-end and non-functional tests integrated into CI/CD. Collaborate with engineers, product and architects to embed shift-left testing and quality by design, contribute to tooling, perform root-cause analysis, participate in code reviews and continuously evaluate tools to improve quality engineering practices.

Develop, evolve and amplify the employer brand through content creation, channel management (LinkedIn, Glassdoor, careers site), thought leadership support, event coordination, projects (merchandise/partnerships), and measurement of outcomes against targets within budget.

Own end-to-end delivery of production ML and LLM-powered systems: design and build training/deployment pipelines, package and deploy models, implement monitoring for performance and drift, support batch and real-time workloads, evolve internal ML platform and tooling, promote best practices, and provide technical leadership and mentoring across data science and engineering teams.

Build, deploy, and maintain production ML and AI systems: model packaging, lifecycle automation, monitoring, batch and real-time workloads, and platform improvements while collaborating with data scientists, engineers, and product teams.

Design, operate, and optimise highly available database and storage systems to improve reliability, performance, scalability and observability. Lead platform improvements, automate operations, support data pipelines, participate in incident response and root cause analysis, and collaborate with engineering and data teams to evolve platform capabilities.

Lead a data science team building always-on predictive models and pipelines for an AI platform. Own model lifecycle: production deployment, monitoring, drift detection, retraining, and measurable business outcomes. Provide technical leadership, code reviews, architecture decisions, and stakeholder collaboration. Hire and mentor data scientists, champion responsible and auditable AI, and adopt ML platform tooling (MLflow, model registries, Databricks) and LLM/RAG approaches where appropriate.