Senior Data Scientist

Are you ready to join the future of innovation at NXP?

As a Data Scientist, you will directly contribute to increasing the speed and cost efficiency of NXP’s New Product Introductions. Your work will enable data driven decision making in R&D through high quality insights, automated reporting, and scalable analytics products.

Working closely with data engineers and data scientists, you will help define the future of R&D analytics at NXP. You build robust, automated, and user friendly dashboards and reports that empower our R&D community with self service business intelligence capabilities.

This is what you will do as Data Scientist at NXP

As part of the Hardware Design Analytics team, you will innovate and evolve the analytics products that support our R&D organization. You will have plenty of room for new ideas, experimentation, and technology improvements collaboration is at the core of how we work.

Your key responsibilities

• Stakeholder Collaboration: Work with project managers, resource managers, IT teams, and other stakeholders to gather requirements, define project scope, and ensure alignment with business objectives.

• Data Analysis: Collect, analyze, and interpret datasets from R&D projects to uncover trends, key performance indicators, risks, and opportunities. Translate complex data into meaningful business insights through reliable, high quality visualizations and clear analytical storytelling.

• Dashboarding & Reporting: Design, develop, and maintain powerful and trustworthy dashboards and automated reports using Python. Ensure that visualizations clearly communicate insights and support impactful decision making. Gather user feedback and iterate to continuously improve usability and impact.

• Machine Learning & AI Development: Build and deploy ML models (e.g., forecasting, NLP, optimization, automation) to improve R&D efficiency and quality. Ensure proper evaluation, explain ability, and production monitoring.

• Data Engineering Support: Collaborate with data engineers to improve data pipelines, data quality, and data models used for reporting and analytical products.