Asks for

The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Able to work in virtual environment and comfortable with git (Github) processes, including PRs and code conflict resolution.”
The ad says“Able to work in virtual environment and comfortable with git (Github) processes, including PRs and code conflict resolution.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”

Job Description

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.

Qualifications

Bachelors in Data Science, Mathematics, Statistics, Engineering, Computer Science, or related field & experience, 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.

Preferred:

Experience in NIQ processes and methodologies, such as data collection, platforms, research processes, and operations.

Additional Information

Our Benefits

Flexible working environment
Volunteer time off
LinkedIn Learning
Employee-Assistance-Program (EAP)

NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://www.nielseniq.com/global/en/ai-safety-policies.

About NIQ

NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population.

For more information, visit NIQ.com

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Our commitment to Diversity, Equity, and Inclusion

At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence. All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news-center/diversity-inclusion