A Bit About Us

At Gamma, we’re more than just a leader in Unified Communications as a Service (UCaaS). We’re a fast-moving technology organisation delivering innovative voice, data, and mobile solutions across Europe.

Our product and service portfolio is at the heart of what we do — powering reliable, scalable communications for thousands of businesses. Behind the scenes, our engineering teams design, build, and maintain the software systems that generate real business impact.

We combine the agility of a start-up mindset with the stability of a leading European business. Collaboration, ownership, and continuous improvement define how we work — and we’re always looking for people who want to build meaningful technology at scale.

Who are we looking for?

Design, build, and own end‑to‑end, Snowflake‑based machine learning and advanced analytics solutions that transform telecommunications data into trusted models, insights, and predictions driving customer retention, growth, and commercial decision‑making across the business.

What will you be doing day-to-day?

• Own the end‑to‑end machine learning lifecycle, from problem definition and exploratory analysis through model development, deployment, monitoring, and iteration, delivering production‑grade ML solutions with measurable business impact.
• Define, design, and continually improve business‑critical “gold” analytics data models on Snowflake, ensuring they represent consistent, trusted views of customers, products, revenue, and usage, and serve as the single analytical truth across reporting, analytics, and ML use cases.
• Develop, evaluate, and deploy machine learning models relevant to a telecommunications environment, including customer churn prediction, early‑warning indicators, lifetime value forecasting, segmentation, and recommendation or next‑best‑action systems.
• Ensure analytical datasets, gold data models, and ML outputs are secure, reliable, and performant, applying Snowflake best practices such as RBAC, query optimisation, cost‑efficient compute usage, and appropriate materialisation strategies.
• Work closely with product, engineering, marketing, finance, and commercial stakeholders to align on definitions, metrics, and analytical priorities, translating ambiguous business problems into well‑scoped analytics and ML solutions.
• Mentor and uplift the existing data squad responsible for ingestion and transformation, setting standards for analytical data modelling, data quality, and ML‑aware design, and acting as a senior technical authority across the data organisation.