Principal Data Scientist

Principal Data Scientist – VIAVI Solutions

Overview:

VIAVI Solutions is seeking a Principal Data Scientist to lead the data science and MLOps strategy for our next-generation NITRO AIOps 2.0 platform. This role is critical in shaping the platform’s intelligence layer, ensuring robust, scalable, and production-ready AI/ML capabilities that support cross-domain network assurance, automation, and analytics for global telecom operators.

As a senior technical leader, you will guide both the algorithmic direction and the deployment architecture of AI/ML solutions. You will collaborate across R&D, product management, and customer-facing teams to ensure that data science initiatives align with business goals and are seamlessly integrated into the platform’s architecture.

Key Responsibilities:

• Lead the design and implementation of AI/ML models for anomaly detection, forecasting, and incident prediction.

• Architect and optimizeMLOps pipelines for scalable, automated model training, validation, deployment, and monitoring.

• Select and evaluate appropriate algorithms and statistical methods for diverse telecom data sets.

• Define best practices for data preprocessing, feature engineering, and model interpretability.

• Collaborate with software architects to integrate ML models into a cloud-native, microservices-based platform.

• Guide the selection and integration of MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).

• Ensure data quality, governance, and compliance across the ML lifecycle.

• Mentor global data science and engineering teams, fostering a culture of experimentation and rigor.

• Partner with product and engineering teams to align data science initiatives with customer needs and business strategy.

• Stay ahead of emerging trends in AI/ML, MLOps, and telecom analytics to drive innovation.

Qualifications:

• Bachelor’s or Master’s in Computer Science, Data Science, Statistics, or a related field.

• 15+ years of experience in data science, analytics, or related fields, with 4–5 years of hands-on experience in machine learning and MLOps.

• Deep expertise in statistical modeling, time-series analysis, and data-driven decision-making.

• Proven experience deploying ML models in production using MLOps frameworks.

• Strong programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker).

• Familiarity with telecom domains (RAN, Core, Transport) and associated data types.

• Excellent communication and cross-functional collaboration skills.

Preferred:

• Experience with AI observability, model drift detection, and continuous learning systems.

• Background in telecom assurance or AIOps platforms.

• Knowledge of TMF standards and SaaS architectures.

• Contributions to open-source MLOps or data science tools.