NielsenIQ
Market Research
DataScientist
Neural analysis suggests this role is
optimal for mid candidates.
“Data Scientist at NielsenIQ. Skills: Data Science, Machine Learning, Data Engineering, Business Intelligence. Develop and implement machine learning models. Build and maintain data pipelines”
Industry & Context.
Analytical skills
What They're Looking For.
Must Have
Bachelor's degree in Statistics, Computer Science, Mathematics, or related field, 5+ years of experience in data science or analytics, Proficiency in SQL, Experience with Python or R
Nice to Have
Master's degree or PhD in a quantitative field, Experience with cloud platforms (AWS, GCP, Azure), Experience with ML frameworks (scikit-learn, TensorFlow, PyTorch), GCP Professional Data Engineer certification, AWS Data Analytics certification, Databricks Certified Associate, Dbt Certified Associate
What You'll Do.
Develop and implement machine learning models
Build and maintain data pipelines
Design and optimize data warehouses
Create and manage BI dashboards
Perform statistical analysis
Collaborate with stakeholders to understand data needs
Ensure data quality and integrity
Stay updated on industry trends and technologies
How You'll Work.
Team & Collaboration
Cross-functional teams; Business stakeholders
Full Job Description
The Data Scientist role focuses on designing and maintaining statistical frameworks that power Retail Panel data and market measurement solutions. This includes building and managing universe definitions, developing extrapolation methodologies, and ensuring high data quality and reliability across Point-of-Sale (PoS) datasets. This role plays a critical part in transforming sample-based retail data into accurate market-level insights used by clients for business decision-making. The position requires strong expertise in data analysis, statistical modeling, and quality control processes, along with a solid understanding of large-scale data production systems. 1. Methodology and Universe Studies: * Design and manage universe studies, including project management. * Document methodologies for transparency and provide clear explanations of existing methods. * Review results of universe studies and insert changes in the POS Manager. * Conduct impact analysis of universe studies. * Design and simulate extrapolation setups for new panels and sign off on the designs. 2. Quality Assurance: * Optimize panel samples through shop contribution analysis and MDQC(Market Data Quality Check) outlier detection * Ensure target sample requirements are consistently met * Identify and resolve data issues (e.g., inactive shops, low sales performance) * Detect systemic methodological issues and drive end-to-end remediation plans 3. Modeling and Extrapolation: * Develop and maintain advanced models for missing retailers outside core systems * Support system integration of models and transition ownership to Stat Ops * Lead new country/channel setups, including: Channel definition validation, Universe and sample design, Clear implementation guidelines 4. Quality Monitoring: * Resolve complex (Level 2) methodology-related queries * Monitor KPIs related to panel health and data quality * Conduct root cause analysis (RCA) across key areas: Samples, Missing retailers, Universes, Copy shops, MDQC(
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