• Apply advanced analytics and machine learning to broadband access network, service, and subscriber telemetry data. • Work closely with senior data scientists, engineers, and product teams to build models and insights that power network intelligence, service assurance, and subscriber experience analytics within Calix’s cloud platforms. • Analyze and model large-scale broadband telemetry and time-series data used by Calix Cloud. • Develop and validate ML models for Upsell, cross-sell, churn prevention, customer acquisition, anomaly detection, performance forecasting, fault classification, and capacity prediction. • Collaborate with data engineering and platform teams to develop and integrate models into Calix Cloud’s cloud-native analytics pipelines. • Communicate insights clearly to product, engineering, and customer-facing teams via dashboards, reports, and presentations.

• Analyze and model large-scale broadband telemetry and time-series data used by Calix cloud, including throughput, latency, packet loss, utilization, and device-level metrics, and many more. • Develop and validate ML models for Upsell, cross-sell, churn prevention, customer acquisition, anomaly detection, performance forecasting, fault classification, and capacity prediction that drive proactive network insights. • Build features and models supporting network health scoring, service quality monitoring, and subscriber Quality of Experience (QoE) analytics. • Apply advanced techniques such as time-series modeling, change-point detection, and probabilistic modeling to real-world broadband data. • Collaborate with data engineering and platform teams to develop and integrate models into Calix Cloud’s cloud-native analytics pipelines. • Perform EDA, feature engineering, and data preprocessing for scalable, production pipelines. • Help scale analytics and ML solutions across millions of access devices, subscriber endpoints, and Wi-Fi environments. • Design experiments and evaluate the business and operational impact of analytics on network performance and subscriber experience. • Build scalable ML pipelines and deploy models into production environments. • Communicate insights clearly to product, engineering, and customer-facing teams via dashboards, reports, and presentations. • Translate ambiguous product and operational problems into well-defined data science and ML solutions. • Follow best practices in model lifecycle management, including versioning, validation, and deployment monitoring.

• Analyzing and interpreting data sets to provide insights which will help make the performance marketing more efficient • Active collaboration with the UA team to adjust strategies based on data-driven insights • Build and maintain LTV forecasting models • Identify new signals (user behavior) for UA • Communicate complex data analysis and insights to non-technical stakeholders • Develop and maintain data pipelines to ensure accurate and timely data processing • Stay up-to-date with the latest technologies and advancements in Marketing Analytics

• uncovering strategic insights by combining deep product knowledge with rigorous data analysis • designing and analysing experiments to test hypotheses • defining, forecasting and influencing with metrics • supporting roadmap planning by surfacing insights