Lead Data Scientist - Banking

hackajob is collaborating with Virgin Media to connect them with exceptional professionals for this role. \n Lead Data Scientist – Network Investment Planning & Optimisation \n Location: London (Hybrid) Reports To: Head of Data Science \n About Virgin Media O2 \n Virgin Media O2 (VMO2) is one of the UK’s largest telecommunications providers, connecting over 46 million customers across mobile, broadband, and TV. We are delivering one of the largest investments in UK digital infrastructure in a generation, allocating hundreds of millions of pounds annually to build the networks of the future—including ultrafast fibre and next-generation 5G. \n To power this massive capital investment, we are leveraging cutting-edge AI, machine learning, and mathematical optimisation to ensure every pound is invested where it can deliver the maximum benefit. \n We are looking for a Lead Data Scientist to join our Network Investment team. In this role, you will be the technical lead driving the design, development, and deployment of optimisation and predictive planning tools that shape our capital investment strategy. \n You will lead a high-performing team of data scientists to build intelligent, automated models that simulate rollout scenarios, and forecast and optimise their impact on key business measures. n · Lead & Mentor: Act as the technical lead for a team of data scientists. Provide mentorship, steer project architectures, and champion high standards for clean, reproducible, and production-grade code. \Design and implement models to simulate our networks and forecast how our customers use them and the service quality they will receive. Leverage these to solve crucial business planning problems. \n · End-to-End Delivery: Own the lifecycle of data science projects from discovery to deployment. Work as part of a cross functional team leveraging Python, dbt, and GCP (Vertex AI, BigQuery) to build robust pipelines. \Partner closely with Network stakeholder to translate complex commercial constraints into data science problems and the solutions to those problems into tangible impact. \n · Educational Background: Master’s degree or PhD in a highly quantitative field or equivalent commercial experience. \Experience leading or mentoring data scientists, setting technical direction, and managing the end-to-end delivery of analytical products. \n · Proven Problem-Solving Record: Proven track record of using mathematical, ML and software techniques to solve industrial problems and deliver business impact. \n · Modern Tech Stack: Advanced proficiency in Python (pandas, NumPy, scikit-learn) and SQL. \