Senior Data Scientist (Telecommunications) – 100% Remote

About the role

We are looking for an experienced Senior Data Scientist to lead the design and deployment of predictive models, machine‑learning pipelines and generative‑AI solutions for a telecommunications ecosystem. The role is fully remote and will work closely with commercial, marketing, data‑engineering and BI teams to turn business questions into data‑driven actions.

Key responsibilities

• Architect, build, evaluate, and deploy end‑to‑end machine‑learning models such as churn prediction, recommendation systems, propensity modeling, price elasticity, and customer segmentation.
• Explore, prototype, and implement generative AI and agentic AI workflows to enhance customer analytics, decision automation, and data‑science operational efficiency.
• Write highly optimized SQL queries and create data pipelines on enterprise data warehouses (primarily Teradata) and cloud analytics platforms.
• Design personalization and recommendation logic based on usage patterns, business rules, and ranking metrics.
• Partner with commercial, marketing, data‑engineering, and BI teams to translate strategic business questions into actionable data‑science solutions.
• Establish MLOps best practices, including model validation, feature‑store management, experiment tracking, and continuous performance monitoring in production.

Required profile

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering or a related quantitative field.
• Minimum 5 years of hands‑on data‑science experience in production environments.
• Strong preference for experience in the telecommunications industry, with knowledge of ARPU, subscriber lifecycle, churn dynamics, network usage, and offer bundles.
• Demonstrated success working effectively in a fully remote, asynchronous, cross‑functional environment.

Required skills

• SQL (highly optimized queries)
• Teradata data warehouse
• Generative AI (LLMs, Retrieval‑Augmented Generation, prompt engineering)
• Agentic AI / autonomous agents
• Machine‑learning model development and deployment
• MLOps practices (model validation, feature store, experiment tracking)