Principal Research Scientist - AI & Data Science

You will work with an interdisciplinary and global R&D team dedicated to the research, development, and deployment of new statistical/ML/AI methodologies to support a range of NIQ products/processes enhancements from within in the Data Science organization. We are looking for someone who can leverage advanced technologies – including statistical methods, machine learning, and artificial intelligence – to improve products/processes and, coupled with strong analytical skills, successfully deliver on R&D tasks.

You must be a scientist at heart and have a passion for innovation, research & development (R&D), and challenging problems!

Responsibilities

• Research and develop new methodologies, research directions, prototype solutions, and quantify their improvement with rigorous scientific methods in the ML/AI space.

• Own all the phases of an R&D project or process improvement, including conceptualization, design, prototyping, documentation, deployment, and monitoring.

• Work closely and collaborate with experienced teams in operations, technology, other data scientists, and internal stakeholders during all phases of a project.

• Deliver high quality, academic publication-level, documentation of new methodologies and best practices.

• Engage with stakeholders on objectives, scope, execution, data exchange, and outcomes for assigned projects.

• Participate in and actively contribute to multiple projects simultaneously.

• Doctorate (Ph.D.) Degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or related field, with outstanding analytical expertise and strong technical skills. At least 7 years of relevant experience.

• Domain expert knowledge in multiple areas of the following: programming, software engineering / UI & UX / multivariate statistics (parametric/non-parametric) / machine learning / deep learning & Agentic AI / trend & time-series analysis / sampling theory.

• Critical and innovative thinking coupled with strong analytical skills focused on experimentation and hypothesis testing.

• High proficiency in Python programming (without AI assistance) and working with large-scale databases (e.g., SQL, Hadoop, pySpark, etc), statistical packages (Pandas, NumPy, Scikit-Learn), and unit testing. Experience with AzureML, DataBricks, and CI/CD (Docker, Unix, AKS) best practices.

• Able to work in virtual environment and comfortable with git (Github) processes, including PRs and code conflict resolution.

• Strong communication, presentation and collaboration skills in the English language with a record of written materials and public presentations (e.g., papers, conferences, patents, tutorials, substack, etc).

• A continuously learner willing to experiment and adopt new technologies and tools.