Data Scientist - AI and LLMs - People Analytics

Job Title Data Scientist - AI and LLMs - People Analytics Job Description Job Description Summary 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.

In this role, you have the opportunity to Shape evidence-based data, analytics and AI solutions that help Philips teams make better decisions and improve outcomes.

As part of the People Analytics Development Pod, you will partner with business stakeholders, Data Engineers and Full Stack AI Application Engineers to translate ambiguous questions into robust analytical approaches, models and AI-enabled product capabilities.

While embedded in People Analytics, you will contribute to products and use cases with relevance across Philips.

You are responsible for 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 Development Pod within the People Intelligence organisation.

The pod builds high-value digital products, AI-enabled applications, automations and analytics experiences.

The Data Scientist brings analytical rigour and AI expertise to the pod, working closely with colleagues across People Intelligence and stakeholders throughout Philips.

You will work closely with: 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 To succeed in this role, you will need 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.

Preferred experience Experience with Databricks, including notebooks, SQL warehouses, MLflow, feature engineering, model serving or AI capabilities.

Experience with workforce, talent, HR, commercial, operational or other business analytics.

Experience with forecasting, workforce planning, optimisation, experimentation or causal inference.

Experience with Azure OpenAI, Azure AI services or another enterprise GenAI platform.

Experience with NLP, text analytics, embeddings, vector search or conversational systems.

Experience designing human evaluation, feedback or adoption-measurement processes for AI p