### 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. Onsite roles require full-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations. this role is an **office** role. **About Philips** We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others. • Learn more about [our business](https://www.philips.com/a-w/about.html). • Discover [our rich and exciting history](https://www.philips.com/a-w/about/our-history.html). • Learn more about [our purpose](https://www.philips.com/a-w/about/environmental-social-governance/our-purpose). If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care [here](https://www.careers.philips.com/global/en/our-culture).