Summary:
In new product design roles: develops and programs integrated software algorithms to structure, analyze and leverage data in product and systems applications in both structured and unstructured environments. Develops and communicates descriptive, diagnostic, predictive and prescriptive insights/algorithms. In product/systems improvement projectsDuties & Responsibilities:
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 optimize MLOps 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.
Pre-Requisites / Skills / Experience Requirements:
