[Remote] Staff Data Scientist

Note: The job is a remote job and is open to candidates in USA. Calix is a company focused on transforming Communication Service Providers' businesses through innovative cloud solutions. The Staff Data Scientist will apply advanced analytics and machine learning to broadband access network data, collaborating with teams to build models that enhance network intelligence and subscriber experience.

Responsibilities

• 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

Skills

• PhD in Data Science or Computer Science
• 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 classification, Clustering and dimensionality reduction, Time-series analysis and forecasting, Anomaly detection and change-point detection
• Experience with model evaluation, validation methods, and performance metrics
• Strong programming skills in Python and familiarity with ML libraries NumPy, pandas, SciPy, scikit-learn
• Strong SQL skills for large-scale data analysis
• Ability to write clean, maintainable, and testable code
• Experience with data preprocessing, feature engineering, and exploratory data analysis (EDA)
• Experience in analyzing broadband network and service telemetry
• Ability to work with metrics such as latency, throughput, packet loss, utilization, and device-level signals
• Understanding of noisy, incomplete, and delayed data common in broadband environments
• Ability to reason about data across devices, subscribers, locations, and time windows
• Strong problem-solving skills and the ability to translate ambiguous product or network problems into analytical solutions
• Clear written and verbal communication skills
• Experience presenting insights through charts, dashboards, and reports
• Experience or research in broadband access networks, subscriber analytics, or network intelligence
• Familiarity with Calix-relevant broadband technologies such as Fiber (PON), Cable (DOCSIS), and Wi-Fi telemetry
• Experience with cloud-native data platforms (AWS, GCP, Azure) and ML deployment frameworks
• Exposure to MLOps practices, including CI/CD, model monitoring, and lifecycle management
• Knowledge of real-time analytics, streaming data, or large-scale data ecosystems
• Publications or applied research in network analytics, anomaly detection, forecasting, or machine learning

Benefits

• Bonus

Company Overview

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