Data Scientist – AI & LLMs for People Analytics

About the role

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. 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.

Key responsibilities

• Frame complex business questions as measurable analytical, statistical, machine‑learning or AI problems.
• Explore, prepare and analyse data to identify insights, patterns, risks and opportunities.
• Design and develop predictive, descriptive, diagnostic and prescriptive models for People Analytics and broader Philips use cases.
• Apply robust statistical methods such as hypothesis testing, segmentation, forecasting, regression, classification, causal inference or experimentation.
• Prototype and evaluate LLM‑enabled solutions, including prompt design, retrieval approaches, grounding, citations, guardrails and human‑in‑the‑loop workflows.
• Collaborate with Data Engineers and Full‑Stack AI Application Engineers to translate analytical capabilities into scalable applications.
• Use Databricks and related AI capabilities to develop, test and operationalise analytical solutions.
• Document methodologies, assumptions, model performance and evaluation results, and communicate findings to technical and non‑technical stakeholders.

Required profile

• A bachelor’s or master’s degree in data science, statistics, economics, mathematics, computer science, engineering, behavioural science or a related field.
• Strong professional experience in data science, advanced analytics, statistics, machine learning or a comparable quantitative discipline.
• Hands‑on Python and SQL skills for data analysis, modelling and reproducible workflows.
• Solid grounding in statistical analysis, model validation and experimental or quasi‑experimental methods.
• Experience developing predictive or analytical models and translating results into business decisions.
• Practical experience with AI or LLM‑enabled solutions such as prompt workflows, semantic search, retrieval‑augmented generation, text analytics or conversational interfaces.
• Understanding of LLM evaluation, including relevance, factuality, consistency, safety and business usefulness.
• Experience with modern data and AI platforms, preferably Databricks.

Required skills

• Python
• SQL
• Databricks
• Machine Learning
• Statistical Modeling
• Large Language Models (LLMs)
• Prompt Engineering
• Semantic Search
• Retrieval‑Augmented Generation (RAG)
• Data Visualization