Lead Data Scientist
NielsenIQ View all jobs
- Pune, Maharashtra
- Permanent
- Full-time
- 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.
- Masters (M.Sc.) or Doctorate (Ph.D.) degree 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.
- Experience in NIQ processes and methodologies, such as data collection, platforms, research processes, and operations.
- Flexible working environment
- Volunteer time off
- LinkedIn Learning
- Employee-Assistance-Program (EAP)