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 . 5) Analytics Products, Dashboards & Data Governance • Own key analytics outputs as products ( dashboards , reusable datasets , internal tools ), continuously improving them based on usage patterns and performance gaps . • Build and automate dashboards and analytical components using scalable SQL logic, Python transformations, and reusable modules . • Act as owner for critical commercial/syndicated datasets (e.g., GfK, Circana, Nielsen or equivalent): definitions , assumptions , and limitations , ensuring transparent logic and trust in outputs. • Partner with data engineering/IT to ensure data quality , harmonization , and governance through strong validation and reconciliation practices. 6) Stakehold