Lead Data Scientist

Job Title Lead Data Scientist Job Description Job title: Lead Data Scientist Your role: The Lead Data Scientist architects, builds, and runs production-grade Machine Learning and Generative AI systems—owning the full lifecycle from model development to scalable cloud deployment and ongoing performance monitoring. In addition, the role partners with commercial stakeholders translate market/customer data into decision-ready insights and AI-enabled analytics solutions that drive measurable outcomes Operating with a builder and translator mindset, the individual rapidly develops MVP analytics solutions, leverages AI to accelerate insight generation, and ensures strong product engineering fundamentals, data quality, and governance. The role plays a critical part in establishing a single source of truth for performance management across markets and channels while elevating analytics maturity from descriptive reporting to predictive and insight-led decision making. Key Responsibilities 1) ML & Deep Learning Model Development Design, train, and optimize ML models for prediction, classification, ranking, time-series forecasting, anomaly detection, NLP, and recommendation use cases. Build robust experimentation workflows (train/validation strategy, ablations, error analysis) and improve model quality through iterative tuning. Ensure reproducibility and maintainability through clean code practices, versioning, and automated testing. 2) GenAI Engineering (LLMs, RAG / MCP / fine-tuning, Agents) Build enterprise-grade LLM applications using RAG (retrieval-augmented generation), MCP, and fine-tuning approaches: chunking strategies, embedding generation, hybrid retrieval, reranking, prompt templates, and citation/attribution patterns. Develop LLM applications with tool use/function calling patterns and agentic workflows where appropriate. Implement systematic evaluation: curated eval sets, prompt regression tests, hallucination checks, retrieval quality metrics, and automated quality gates. 3) ML & LLM Operations: Productionization, Deployment & Monitoring Deploy and operate real-time and batch inference solutions on Azure using managed endpoints and/or containerized serving. Build CI/CD for ML systems: automated packaging, container builds, model validation tests, staged rollouts, and rollback strategies. Establish lifecycle management: model registry/versioning, lineage, promotion workflows, and release governance. Implement observability: latency, throughput, cost, drift signals, data quality checks, alerts, and performance degradation monitoring. 4) Pipeline Orchestration & Automation (Train → Deploy) Build standardized ML pipelines for training, evaluation, and deployment using orchestration tools (cloud-native pipelines and/or platform tools). Automate dataset/version management, feature generation, scheduled retraining triggers, and approval workflows. Define repeatable patterns for scalable experimentation and reliable production delivery.