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.
Discover our rich and exciting history.
Learn more about 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.
