Jobs at CTM Data & Electrical

Develop and amplify employer brand through content creation, social channels and events. Curate and publish content on LinkedIn, Glassdoor and careers site, support thought leadership, coordinate employer brand activity at events, manage projects to budget, and measure outcomes to improve recruitment and talent engagement.

Lead architecture and delivery of reusable platform capabilities, driving technical strategy, Spec Driven Development, and AI-assisted tooling adoption. Own end-to-end outcomes across teams, mentor senior engineers, simplify the technical estate, and ensure scalable, secure, observable, high-performance systems.

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.

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.

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.

Lead design and deployment of ML models and pipelines for the AI platform. Translate business problems into measurable ML solutions, design experiments and A/B tests, and own model performance, monitoring, degradation detection and retraining. Champion responsible, explainable AI in a regulated financial context, choose between classical ML, LLMs and agentic approaches, collaborate with ML engineering, product and commercial teams, and mentor junior data scientists.

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.

Lead technical direction and delivery for Enterprise Services: define strategy and architecture, own end-to-end outcomes, run cross-team initiatives, de-risk complex deliveries, mentor senior engineers, partner with product and ops, manage vendor relationships, and ensure security, compliance, and scalable automation across enterprise platforms.

Design and deliver high-performing email campaigns and templates; own end-to-end email design from concept to build-ready assets; develop and maintain the email design system in Figma; collaborate with CRM, marketing and development for implementation; support A/B testing and optimisation; ensure responsive, accessible, and deliverable designs across email clients and devices.

Lead and grow a team of ML engineers to build and operate production ML and LLM systems—real-time inference APIs, batch data products, and agentic AI—ensuring deployment, monitoring, CI/CD, reproducibility, and measurable business impact.

Lead, coach and grow a high-performing software engineering team. Own technical health, reliability and maintainability of customer-facing systems. Partner on architecture and technical strategy, collaborate with Product to balance delivery and technical debt, champion Agile and AI-augmented engineering practices, and ensure alignment with governance, security and risk requirements.

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.

Lead and develop data engineering teams building modern data platforms and large-scale pipelines. Drive technical discussions, improve engineering practices, ensure delivery alignment with product and roadmap, manage incidents, and support hiring, coaching, and cross-functional stakeholder communication to optimise team performance and operational excellence.

Design, build and maintain DBT models, transformation pipelines and analytics workflows. Create BI dashboards, implement data quality checks, optimise data models, support platform migrations, and collaborate with stakeholders to deliver reliable, scalable analytics solutions.

Provide first-line governance for AI systems across lifecycle: assess and test models (including generative and agentic systems), produce technical risk reports and metrics, advise on 'License to Operate' decisions, consult on high-risk system design, and partner with Data Science, ML Engineering, Risk & Compliance to evolve governance standards.

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.

Partner with cross-functional product teams to analyse large behavioural datasets, define product metrics, design and evaluate A/B tests, build measurement frameworks, and deliver insights via visualisations to influence product roadmaps and drive commercial outcomes.

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.

Lead and optimise paid digital campaigns across social, display, affiliates, and app acquisition channels (Meta, TikTok, Apple, Google). Improve performance via audience targeting, creative testing, bidding and budget optimisation. Analyse metrics (CPI, CPA, ROAS), collaborate with creative, product, analytics and agencies, and scale paid media to drive efficient customer acquisition and app installs.