Overview

Develop rigorous data-science, machine-learning and AI solutions that turn People and business data into trusted insights, predictions and decision-support products for Philips.

Responsibilities

• Framing complex business questions as measurable analytical, statistical, machine-learning or AI problems.
• Exploring, preparing and analysing data to identify insights, patterns, risks and opportunities.
• Designing and developing predictive, descriptive, diagnostic and prescriptive models for People Analytics and broader Philips use cases.
• Applying robust statistical methods, including hypothesis testing, segmentation, forecasting, regression, classification, causal inference or experimentation, where appropriate.
• Designing clear analytical narratives, visualisations and recommendations that enable business stakeholders to make confident decisions.
• Bringing an AI and LLM specialism to the team, identifying where GenAI, retrieval-augmented generation, semantic search, natural-language interfaces or automation can create meaningful value.
• Prototyping and evaluating LLM-enabled solutions, including prompt design, retrieval approaches, grounding, citations, guardrails and human-in-the-loop workflows.
• Designing structured evaluation approaches for AI and LLM solutions, including relevance, factuality, completeness, safety, fairness and user usefulness.
• Selecting the appropriate solution approach for each problem, including statistics, machine learning, GenAI, search, automation and rules-based logic.
• Collaborating with Data Engineers to define data requirements, data quality expectations and reusable analytical datasets.
• Collaborating with Full Stack AI Application Engineers to translate analytical and AI capabilities into intuitive, scalable applications.
• Using Databricks and related data and AI capabilities to develop, test and operationalise analytical solutions where relevant.
• Applying privacy, security, responsible-AI and compliance requirements when working with sensitive employee or business data.
• Documenting methodologies, assumptions, model performance, evaluation results and limitations.
• Communicating complex findings and technical trade-offs clearly to technical and non-technical stakeholders.
• You are a part of
• The People Analytics Lead and People Intelligence Analytics Partners
• Data Engineers
• Full Stack AI Application Engineers
• Product owners and business stakeholders across Philips
• Enterprise IT, cloud, architecture and security teams
• Privacy, compliance and responsible-AI teams
• A bachelor's or master's degree in data science, statistics, economics, mathematics, computer science, engineering, behavioural science, quantitative social science or a related field.
• Strong professional experience in data science, advanced analytics, statistics, machine learning or a comparable quantitative discipline.
• Strong hands-on Python and SQL skills for data analysis, modelling and reproducible analytical workflows.
• Strong grounding in statistical analysis, model validation and experimental or quasi-experimental methods.

Qualifications

• Familiarity with machine-learning techniques and their appropriate use, limitations and evaluation.
• Practical experience with AI or LLM-enabled solutions, such as prompt workflows, semantic search, RAG, text analytics or conversational interfaces.
• Understanding of LLM evaluation, including how to assess relevance, factuality, consistency, safety and business usefulness.
• Ability to communicate analytical concepts, uncertainty, assumptions and recommendations clearly to non-technical audiences.
• Strong business acumen, curiosity and the ability to turn ambiguous stakeholder questions into rigorous analytical work.
• Understanding of privacy, fairness, explainability and responsible-AI considerations.
• The ability to work independently while collaborating effectively in a global, matrixed environment.
• Key capabilities