Data Science & AI Lead

Job Title

Data Science & AI Lead

Job Description

As a Data Science & AI Lead within Philips, you play a key role in translating business challenges into scalable data‑ and AI‑driven solutions. You operate at the intersection of business, data engineering, and advanced analytics, combining strong technical expertise with stakeholder engagement. In this role, you are responsible for shaping, delivering, and operationalizing AI solutions that create tangible value for Philips Businesses, Functions, customers, and ultimately patients. You contribute hands‑on to solution design and delivery, while guiding teams and partners to ensure robustness, scalability, and alignment with enterprise standards.

In this role, you will:

- Oversee the performance of AI models, ensuring optimization of algorithms to produce accurate user recommendations, insights, and outputs that align with business objectives and needs.

- Lead statistical analysis and fine-tuning of AI models using test results and validation techniques to validate assumptions, optimize model parameters, and improve predictive accuracy.

- Direct data mining initiatives, including algorithm development, data collection, and preprocessing activities, throughout the design and implementation phases, ensuring high-quality and relevant data.

- Drive the deployment and testing of AI solutions and insights, overseeing implementation efforts, conducting rigorous testing procedures, and validating system functionality and performance.

- Serve as an expert in AI DevOps practices and technologies, shaping methodologies and approaches to effectively manage, automate, and deploy AI models across development and production environments.

- Architect MVP applications that cover the entire lifecycle of AI model development, ensuring scalability, reliability, and performance optimization from initial prototyping to final deployment.

- Evaluate emerging open-source data science and machine learning libraries, assessing their suitability and potential impact on AI initiatives, and recommending adoption strategies.

- Define data needs based on business strategy, developing acquisition plans, data management strategies, and governance frameworks to ensure data availability, quality, and integrity.