QPC Head of Data & AI

*Direct applications only. QPC is recruiting for this role in-house and is not accepting approaches, candidate submissions or speculative contact from recruitment agencies or third-party suppliers. Unsolicited CVs will not be accepted and no fee will be payable.* QPC is seeking a hands-on Head of Data \& AI to establish and lead the data, analytics, and applied-AI capability behind Tracxion—its contact-centre operational intelligence and integration platform. The role combines technical leadership, product contribution, customer engagement, and delivery accountability, turning complex operational data into reliable, governed capabilities that create measurable customer value. About QPC QPC develops Tracxion, a platform that brings together workforce-management and contact-centre data to provide trusted operational insight, interoperability, and evidence for better decisions. QPC is extending Tracxion with governed analytics, AI and Model Context Protocol (MCP) capabilities that enable customers, partners, and AI assistants to investigate performance safely and effectively. The Role Reporting to the board, the Head of Data \& AI will bring deep practical experience in data science, data products and applied AI into a multidisciplinary product and engineering team. The role will help shape valuable customer problems into robust, supportable analytical and AI-enabled product capabilities. This is not a research-only, reporting-only or strategy-only role. The successful candidate will be expected to lead technically while remaining close to the work: inspecting data, reviewing code and evaluations, contributing to solution design, and stepping into complex Python or SQL work when needed. Technology authority remains with the board and product-roadmap ownership with Product; this role is a senior, influential partner in both decision-making processes. Key Responsibilities · Work with customers, partners, and QPC specialists to understand operational workflows, data sources, business metrics, exceptions and the human judgement involved in decision-making. · Bring data, analytical and industry expertise into product and technical planning, helping QPC prioritise opportunities with clear customer and commercial value. · Translate ambiguous customer and operational needs into well-defined analytical and AI solutions, including intended outcomes, trade-offs, evaluation criteria, risks and human-accountability boundaries. · Provide technical leadership across data analysis, statistical modelling, machine learning, forecasting, anomaly detection, generative AI and decision-support capability. · Contribute directly to technical delivery through Python, SQL, data investigation, design reviews, code reviews and complex problem resolution. · Work closely with Engineering and DevOps to ensure data pipelines, APIs, models and AI services are secure, observable, reliable, scalable, tested and supportable in production. · Establish pragmatic standards for data quality, semantic consistency, privacy, explainability, evaluation, prompt and model testing, access control, auditability and AI safety. · Help define and maintain governed metrics, semantic models and reusable analytical products that customers can trust across systems and channels. · Support major demonstrations, proofs of value, technical proposals and customer deployments, helping establish adoption, time-to-value and measurable operational outcomes. · Turn customer feedback, delivery learning and production incidents into product improvements, reusable delivery patterns, clearer playbooks and improved support tooling. · Coach and develop colleagues across engineering, product and delivery, building transferable capability through clear definitions, documentation, peer review, tests and practical standards. Essential Experience · Demonstrable experience delivering data-science, analytics or AI capabilities into production—not solely prototypes, demonstrations or research environments. · Strong hands-on Python and SQL capability, with the confidence to investigate data, challenge assumptions, assess technical quality and contribute directly when required. · Sound practical knowledge of statistics, experimentation, forecasting, anomaly detection, machine learning, evaluation methods and communicating uncertainty to non-specialists. · Experience developing data products, governing KPIs, semantic models or analytical capabilities rather than only training models or producing reports. · Working knowledge of APIs, cloud deployment, integration patterns, testing, observability, version control and MLOps/LLMOps practices. · Practical applied-AI knowledge, including generative AI, retrieval-augmented generation, tool use and agentic systems; direct MCP experience is advantageous but not mandatory. · Experience applying privacy by design, secure-development practices, access controls, tenant isolation, risk assessment and proportionate AI safeguards in production systems. · Strong customer-facing communication skills, able to explain value, feasibility, risk, limitations and trade-offs to executives, partners, users and technical teams. · Experience leading, mentoring or materially strengthening a multidisciplinary technical team, ideally within a scaling business. Desirable Experience · Contact-centre, customer-service, workforce-management, CCaaS or operational-performance analytics experience. · Experience in B2B SaaS, enterprise software, systems integration or managed services. · Knowledge of cloud data platforms, lakehouse architectures, event-driven or streaming data, and AWS services. · Experience with optimisation, classification, knowledge graphs, retrieval systems, AI evaluation frameworks or decision-support products. · Experience supporting technical pre-sales, customer discovery, proposition development, demos or proofs of value. What Success Looks Like · Tracxion’s roadmap and technical decisions benefit from credible data-science, operational-analytics and applied-AI judgement. · Governed analytical and AI features are deployed into real customer workflows and are reliable, measurable and supportable. · Customers achieve demonstrable adoption, faster time-to-value and improved operational outcomes. · Data quality, evaluation, governance, security and lifecycle controls become routine and proportionate parts of product delivery. · QPC develops durable team capability and avoids critical dependence on undocumented individual knowledge. Why QPC * This is an opportunity to shape a strategic capability at its foundation while working directly with decision-makers, customers, partners, and specialists in contact-centre operations. The role offers genuine influence over the evolution of Tracxion while staying close to meaningful product, technical and customer work.