Staff Data Scientist, Cloud Analytics & ML

Calix; United States; Remote; USD 136,000 - 266,000; Full time

Job summary

Calix is seeking a Staff Data Scientist focused on Calix Cloud and Intelligence to apply advanced analytics and ML to broadband telemetry data. You will work with senior data scientists, engineers, and product teams to build models powering network intelligence and subscriber analytics.

You will develop ML features and models for health scoring, QoE analytics, and anomaly detection, collaborating across cloud-native pipelines and production systems to scale insights for millions of devices and

Qualifications

• PhD in Data Science or Computer Science preferred.
• Strong foundation in statistics, probability and linear algebra.
• Experience with large-scale time-series and telemetry datasets typical of broadband analytics.
• Hands-on ML techniques: regression, classification, clustering, time-series, anomaly detection.

Responsibilities

• Analyze and model large-scale broadband telemetry and time-series data.
• Develop and validate ML models for upsell, churn prevention, anomaly detection and capacity prediction.
• Collaborate with data engineering to deploy models into cloud-native analytics pipelines.
• Perform feature engineering and data preprocessing for scalable pipelines.
• Design experiments to evaluate business impact of analytics on network performance.
• Communicate insights via dashboards and reports to product and engineering teams.

Skills

Analytical thinking

Communication skills

Problem solving

Python programming

Education

PhD in Data Science or Computer Science

Tools

NumPy

pandas

SciPy

scikit-learn

AWS

GCP

Azure

Job description

Calix is seeking a Staff Data Scientist focused on Calix Cloud and Intelligence to apply advanced analytics and ML to broadband telemetry data. You will work with senior data scientists, engineers, and product teams to build models powering network intelligence and subscriber analytics.

You will develop ML features and models for health scoring, QoE analytics, and anomaly detection, collaborating across cloud-native pipelines and production systems to scale insights for millions of devices and