We are seeking a highly experienced Domain Architect-Data Scientist with deep expertise in telecom services, AI/ML, and data management to drive the delivery of data products, intelligent cognitive use cases, and Autonomous Network solutions for Communication Service Providers (CSPs). The role requires strong technical skills, telco domain knowledge, and the ability to engage with both executive and technical stakeholders in a services environment.

 

You Have:

Bachelor’s/Master’s in Computer Science, Statistics, Data Science, Mathematics, or related field.15+ years total experience, including 10+ years in telecom domain and 5+ years in AI/ML & cloud technologies.Strong knowledge of telecom network data (OSS/BSS, CDRs, performance, fault, CX data).Expertise in Python, SQL, PySpark, TensorFlow/PyTorch, Scikit-learn.Hands-on experience with Vertex AI, Red Hat OpenShift AI, Kubeflow, and Kubernetes.Awareness of Generative AI, LLM-Ops, and Agentic AI applications in telecom.MLOps: MLflow, Kubeflow Pipelines, ArgoCD, GitOps.

It would be nice if you also had:

Ab-intio data management platformFamiliarity with network automation, service assurance, and intent-based networking.Exposure to standards: 3GPP, TM ForumPre-sales experience: RFPs, solution demos, customer workshops.Knowledge of Data Mesh, Data Fabric, and MLOps frameworks.Experience with Snowflake, Databricks, Kafka, Flink, Airflow.Visualization: Tableau, Power BI, Grafana.
 

Design, develop, and deliver AI/ML use cases tailored for Autonomous Networks (AN), driving innovation in telecom automation. Build and manage cognitive data products and frameworks that support large-scale AI/ML deployments across telecom environments. Research and apply advanced machine learning techniques to enhance existing systems and solve complex, novel challenges. Collaborate with CSP customers to translate business requirements into scalable, data-driven solutions. Implement end-to-end ML pipelines using platforms such as Vertex AI, Red Hat OpenShift AI, and Kubeflow. Manage the full data lifecycle—from ingestion and feature engineering to model training, deployment, and monitoring. Establish robust frameworks for model performance tracking, drift detection, and automated retraining. Ensure all solutions are aligned with industry standards including 3GPP, TM Forum, and CSP frameworks. Support pre-sales activities, including RFP responses, proof-of-concepts (PoCs), and customer demonstrations. Contribute to reusable assets and delivery accelerators, enhancing the scalability and efficiency of AI/ML use case implementations. Provide strategic direction for identifying new business opportunities and evaluating emerging technology solutions. Act as a senior subject matter expert in data science and machine learning for Autonomous Networks, guiding delivery teams and influencing solution strategy.