Lead Data scientist

Job Description

Job TitleLead Data scientistJob DescriptionJob title:Lead Data scientistYour role:The Lead Data Scientist architects, builds, and runs production-grade Machine Learning and Generative AI systemsowning 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 outcomesOperating 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 Responsibilities1) ML & Deep Learning Model DevelopmentDesign, 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 & MonitoringDeploy 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 GovernanceOwn 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) Stakeholder Partnership & Decision Support (Lightweight, High Impact)Serve as trusted analytics thought partner to senior stakeholders (e.g., BU leadership, Sales, Marketing, Finance), shaping problem statements and aligning on success metrics.Translate complex analytics into clear recommendations with a decision-oriented storyline (so-what / now-what), tailored for leadership forums and reviews.Support performance reviews, planning cycles, and high-priority ad-hoc requests with speed, rigor, and confidence; proactively challenge assumptions with fact-based insights.7) Responsible AI, Security, and Risk Controls (GenAI-ready)Implement guardrails: prompt injection defenses, sensitive data protections, output validation, and secure tool execution patterns.Apply responsible AI practices: transparent evaluation criteria, auditability, and risk controls aligned to enterprise needs.8) Technical Leadership (Lead-level Expectations)Set engineering standards for DS/ML codebases: design docs, code review practices, testing discipline, and production readiness checklists.Mentor data scientists/ML engineers on modeling, GenAI engineering, and MLOps best practices.Lead architectural decisions across modeling approaches, retrieval stack, serving patterns, and evaluation strategy.Core Skills & CompetenciesMust-have (Technical)Strong Python .

Keyskills :
Machine LearningDeep LearningNLPPythonSQLAWSAzureData Science