Sr. / Lead Data Scientist

Responsibilities:

• Work On As a Sr/Lead Data Scientist, you will drive end-to-end development of AI/ML systems across large-scale telecom operations. The role involves solving real-time, high-impact problems across data platforms, industrial computer vision, and enterprise-grade generative AI.
• Data Platform at Scale: Work on ingesting and processing terabytes of telemetry data daily, enabling: Detecting anomalies and generating real-time alerts, Forecasting resource consumption, Predicting asset degradation.
• Build and deploy CV solutions for large-scale telecom infrastructure: Detect defects in infra components, Automate permit-to-work workflows Architect low-latency, low-footprint CV models for edge devices.
• Enable visual inspection and training automation using live AI video. Deploy drone-based inspection systems for remote sites
• Generative AI for the Enterprise Lead strategic GenAI initiatives such as: Building multimodal GenAI capabilities across enterprise data, Developing RAG-based assistants, copilots, and knowledge orchestration systems
• Other Emerging AI Frontiers: Opportunity to innovate in: Autonomous operations using AI agents, Digital twins for infra optimisation, Edge AI for real-time decision-making

Requirements:

• Machine Learning and Deep Learning Strong experience building and deploying ML models at scale.
• Expertise in anomaly detection, forecasting, and time-series modelling.
• Knowledge of CNNs, transformers and advanced DL architectures.
• Computer Vision Expertise Defect detection, image/video analytics, inspection systems Low-latency, edge-optimised model development.
• Exposure to drones or live video-based CV systems (preferred).
• Big Data and Distributed Systems Experience working with massive telemetry datasets (TB-scale), Spark, Kafka, Hadoop, or equivalent big data tools.
• Real-time data processing and alerting pipeline development.
• Generative AI and RAG Systems LLMs, embeddings, vector DBs & RAG pipelines Multimodal GenAI (text + image + video).
• Ability to build AI assistants and enterprise copilots.
• Edge AI Development: Deploying ML/CV models on edge devices, Model optimisation (ONNX, TensorRT, quantisation).
• Strong Data Science Foundations, Statistics, experimentation, feature engineering, End-to-end ML lifecycle & production deployment.
• Software Engineering Skills: Strong Python skills, Clean coding, Git, CI/CD and production ML systems.
• Domain Understanding (Preferred) Telecom, IoT, Infra, Digital Twins, or Predictive Maintenance.
• Leadership and Collaboration Ability to lead AI initiatives independently, Stakeholder communication and mentoring experience.