Data Scientist – AI and LLMs – People Analytics

Role 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.

What You Will Do

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.

Why It Might Be a Fit

You will bring strong statistical and analytical expertise, complemented by a practical specialism in AI and large language models (LLMs), to solve complex business problems responsibly.

Requirements

• 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.
• Experience developing predictive or analytical models and translating their results into business decisions.
• Experience working with structured and unstructured data.
• 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.
• Experience working with modern data and AI platforms, preferably Databricks or an equivalent environment.
• 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.

Benefits

• Dental insurance
• Vision insurance
• Health insurance
• Retirement plan
• Paid holidays
• Flexible work hours
• Professional development opportunities