Telecom operator scaling data and product capabilities for customer retention
TalkTalk operates a multi-service telecom platform (broadband, mobile, TV, telephony) with 201–500 employees based in Salford. The tech stack is heavily Azure/Databricks-native (Python, PySpark, MLflow, Unity Catalog, Azure DevOps) — a mature data platform architecture. Active hiring is concentrated in data (5 roles) and product (2 roles) at senior and lead levels, with projects focused on churn reduction, predictive personalization, and lifetime-value optimization, directly addressing stated pain points around retention and scaling model deployment.
TalkTalk is a UK-based telecommunications company delivering broadband, mobile, TV, and telephony services to residential and business customers. The organization has undergone a strategic rebranding and cultural shift toward customer-centricity and flexibility in the workplace. On the technology side, the company is investing heavily in data infrastructure and product capabilities, with active initiatives spanning digital experimentation, retention at scale, and loyalty programs. The team structure reflects this priority: most open headcount is in data science and product, with particular focus on senior and leadership-level hiring.
TalkTalk's primary stack is Databricks, Azure (including DevOps, AD, Entra ID, Monitor), Python, PySpark, MLflow, Terraform, and Unity Catalog, supplemented by Jira, Confluence, ProductBoard, and Figma for collaboration and product management.
Primary initiatives include digital product experimentation, predictive personalization, churn reduction, and retention/loyalty/cross-sell programs, all aimed at maximizing customer lifetime value and reducing ARPU decline.
TalkTalk's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.