Lead Data scientist

Job description

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 • 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. • 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. • 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. • 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. • 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. • 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. • 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. • 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 & Competencies Must-have (Technical) • Strong Python (production-quality coding) and solid CS fundamentals; strong SQL for data access and validation. • Depth in ML: Traditional ML exposure and at least one deep learning framework (PyTorch/TensorFlow), with strong understanding of metrics and failure modes. • GenAI implementation: RAG / MCP / fine-tuning, embeddings/vector search, prompt orchestration, evaluation harnesses, and LLM application patterns. • Production deployment experience on AWS or Azure (model/LLM app deployment, API serving, scaling, monitoring). • MLOps tooling: experiment tracking, model registry, CI/CD, and pipeline orchestration (e.g., MLflow or equivalent patterns). Good-to-have (Business + Influence) • Strong business acumen and ability to connect disparate data points into compelling narrati