• Drive the long‑term data strategy for the Rentals Shopping pillar • Elevate statistical rigor, modeling, and experimentation practices across Rentals • Partner with product, engineering, and design leaders to translate complex analysis into clear, actionable product and business strategies • Lead analytical and modeling work across critical problem spaces such as rental search relevance, ranking, personalization, or marketplace efficiency • Design and guide robust A/B tests and other experimentation frameworks • Apply modern AI to increase data science velocity and improve model quality • Coach and mentor data scientists

• Utilize NLP techniques to analyze and process textual data, extract insights, and develop models for various NLP applications, such as sentiment analysis, text classification, and information extraction. • Utilize Time Series Forecasting methodologies to analyze historical data patterns, develop predictive models, and generate accurate forecasts for future trends and patterns. • Collaborate with cross-functional teams to understand business requirements and develop data-driven solutions that address specific challenges and opportunities • Communicate findings and insights to stakeholders through data visualizations, reports, and presentations, and provide recommendations for strategic decision-making in the relevant fields. • Continuously evaluate and improve data collection and analysis processes, ensuring data quality, accuracy, and integrity throughout the data lifecycle • Stay updated with the latest trends and advancements in NLP, Computer Vision, and Time Series Forecasting, and recommend innovative approaches, tools, and techniques for data analysis and modeling in these domains. • Work with the data engineering team to design and implement data pipelines and infrastructure that support data analysis and modeling initiatives.

• 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.