Staff Data Scientist - Calix

Role Overview

As a Staff Data Scientist focused on Calix Cloud and Intelligence, you will apply advanced analytics and machine learning to broadband access network, service, and subscriber telemetry data. You will 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.

This role is ideal for PhD candidates with min 5 yrs of experience who want to translate research into production-grade AI capabilities embedded directly into Calix Cloud products.

Key Responsibilities

Calix Cloud - Analytics & AI

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

Calix Cloud - Platform & Scale

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.

Communication & Product Collaboration

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

Required Qualifications & Technical Skills

Education

PhD in Data Science or Computer Science

Data Science & Machine Learning

Strong foundation in statistics, probability, and linear algebra

Experience working with large-scale time-series and telemetry datasets typical of broadband analytics

Hands-on experience with ML techniques, including:

Regression and classi