Design, train, and optimize ML models for prediction, classification, ranking, time-series forecasting, anomaly detection, NLP, and recommendation use cases.
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Build robust experimentation workflows (train/validation strategy, ablations, error analysis) and improve model quality through iterative tuning.
5) Analytics Products, Dashboards & Data Governance
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Own key analytics outputs as products (dashboards, reusable datasets, internal tools), continuously improving them based on usage patterns and performance gaps.
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Build and automate dashboards and analytical components using scalable SQL logic, Python transformations, and reusable modules.
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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.
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6) Stakeholder Partnership & Decision Support (Lightweight, High Impact)
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Serve as trusted analytics thought partner to senior stakeholders (e.g., BU leadership, Sales, Marketing, Finance), shaping problem statements and aligning on success metrics.
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Translate complex analytics into clear recommendations with a decision-oriented storyline (“so-what / now-what”), tailored for leadership forums and reviews.
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7) Responsible AI, Security, and Risk Controls (GenAI-ready)
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Implement guardrails: prompt injection defenses, sensitive data protections, output validation, and secure tool execution patterns.
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8) Technical Leadership (Lead-level Expectations)
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Set engineering standards for DS/ML codebases: design docs, code review practices, testing discipline, and production readiness checklists.
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Mentor data scientists/ML engineers on modeling, GenAI engineering, and MLOps best practices.
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Must-have (Technical)
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Strong Python (production-quality coding) and solid CS fundamentals; strong SQL for data access and validation.
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Depth in ML: Traditional ML exposure and at least one deep learning framework (PyTorch/TensorFlow), with strong understanding of metrics and failure modes.
Production deployment experience on AWS or Azure (model/LLM app deployment, API serving, scaling, monitoring).
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Good-to-have (Business + Influence)
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Strong business acumen and ability to connect disparate data points into compelling narratives that influence senior stakeholders.
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Education Requirements
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Bachelor’s degree in engineering, Computer Science, Statistics, Economics, Mathematics, or a related quantitative field.
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Master’s degree preferred (e.g., Data Analytics, Business Analytics, Applied Statistics, Economics, AI, or MBA with strong analytics focus).
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You're the right fit if:
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Proven track record of owning end-to-end analytics domains, not just contributing to isolated analyses or consuming pre-built reports.
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7–12+ years in hands-on Data Science / ML Engineering with multiple production deployments owned end-to-end.
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Demonstrated ability to take solutions from experimentation → production (reproducible pipelines, deployment to managed endpoints/container platforms, monitoring + iterative improvement).
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Strong GenAI delivery record: shipped RAG/MCP/fine-tuned LLM applications with measurable quality controls, safety measures, and operational readiness.
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Experience operating in complex, matrixed environments and partnering with senior stakeholders to drive insight-led decision making
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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.
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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.
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