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) 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 & 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 narratives that influence senior stakeholders . Builder/MVP mindset — rapid prototyping and iterating based on stakeholder feedback while maintaining data quality and governance Education Requirements Bachelor’s degree in engineering , Computer Science , Statistics , Economics , Mathematics , or a related quantitative field. Master’s degree preferred (e.g., Data Analytics , Business Analytics , Applied Statistics , Economics , AI , or MBA with strong analytics focus). Continuous learning mindset expected, with demonstrated upskilling in advanced analytics , AI , or data engineering concepts (formal or informal). Note: This role values applied problem-solving and business impact over purely academic specialization. You're the right fit if: Proven track record of owning end-to-end analytics domains , not just contributing to isolated analyses or consuming pre-built reports . 7–12+ years in hands-on Data Science / ML Engineering with multiple production deployments owned end-to-end . Demonstrated ability to take solutions from experimentation → production ( reproducible pipelines , deployment to managed endpoints /container platforms, monitoring + iterative improvement ). Strong GenAI delivery record: shipped RAG/MCP/fine-tuned LLM applications with measurable quality controls , safety measures , and operational readiness . Experience operating in complex, matrixed environments and partnering with senior stakeholders to drive insight-led decision making Hands-on exposure to AI-enabled analytics , including the use of GenAI tools (e.g., ChatGPT , Claude , or similar) to accelerate insight generation , analysis, or productivity . Strong experience partnering with senior business stakeholders (BU leaders, Sales, Marketing, Finance), influencing decisions through insight-led storytelling . #Personalhealth How we work together We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week