Nielsen Recruitment 2026
Nielsen
Key Responsibilities
- Identify opportunities where Artificial Intelligence can improve existing and new projects.
- Implement AI-based solutions for relevant business and analytical problems.
- Support reproducible data science projects from end to end.
- Develop and maintain production data pipelines.
- Deploy and maintain machine learning models in production environments.
- Work with cross-functional teams to productionize analytical methodologies.
- Validate and optimize data science methodologies and models.
- Communicate methodology and research findings to technical and non-technical audiences.
- Support research related to cross-platform audience measurement.
- Perform trend analysis and investigate patterns within large datasets.
- Work with missing-data imputation and representation techniques.
- Support sampling and bias-reduction methodologies.
- Work on indirect estimation and data integration problems.
- Explore datasets to identify relevant variables and relationships.
- Clean and prepare large datasets for analysis.
- Apply dimension reduction techniques when appropriate.
- Calculate distances and integrate survey data.
- Evaluate analytical outputs to ensure accuracy and reliability.
- Investigate quality escapes and fix issues in production code.
- Document new methodologies, analytical approaches, and code.
Skills & Eligibility
- Experience: 0–3 years of professional experience.
- Programming: Proficiency in Python.
- Big Data: Knowledge of Apache Spark.
- Cloud: Familiarity with AWS and cloud computing.
- Database: Proficiency in SQL.
- AI/ML: Knowledge of Artificial Intelligence and Machine Learning concepts.
- Statistics: Understanding of statistical concepts and analytical methodologies.
- Data Analysis: Ability to manipulate, analyze, and interpret large datasets.
- Version Control: Experience with Git and GitLab or similar version-control systems.
- Visualization: Familiarity with dashboarding and visualization tools such as Spotfire or Tableau.
- Project Tools: Familiarity with JIRA and Confluence.
- Documentation: Strong ability to document code and analytical methodologies.
- Communication: Strong written and verbal communication skills.
- Teamwork: Ability to work effectively with distributed and cross-functional teams.
- 💡 Pro Tip: This role heavily emphasizes Python and AI/ML. Build stronger Python fundamentals with 100 Days of Code™: The Complete Python Pro Bootcamp, and strengthen your AI/ML knowledge with The AI Engineer Course 2025 before your assessment or technical interview.
- Python is one of the most important technical requirements for the Nielsen AI/ML Data Scientist I role.
- Candidates should be comfortable writing Python programs for data manipulation, analysis, model development, automation, and production workflows.
- Beyond basic Python syntax, candidates should understand common data science concepts such as:
- Data cleaning and preprocessing
- Exploratory data analysis
- Feature engineering
- Statistical analysis
- Model training and evaluation
- Dimensionality reduction
- Missing-data handling
- Model validation
- Data visualization
- Candidates should also understand how machine learning models move from experimentation into production.
Note: This job is posted on external sites. Joblit shares the listing for convenience and does not take responsibility for third-party content.
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