NielsenIQ
Market Research
DataScientist
Neural analysis suggests this role is
optimal for mid candidates.
“Data Scientist at NielsenIQ. Skills: Data Science, Machine Learning, Statistical Modeling. Develop and implement machine learning models. Analyze large datasets to identify trends and insights”
Industry & Context.
Problem Solving; Analytical Thinking
What They're Looking For.
Must Have
5+ years of experience in data science, Bachelor's degree in Computer Science, Statistics, Mathematics, or related quantitative field, Proficiency in SQL, Experience with Python or R for data analysis and modeling
Nice to Have
Master's degree or PhD in a quantitative field, Experience with cloud platforms (AWS, Azure, GCP), Experience with big data technologies (Spark, Hadoop), Experience with machine learning frameworks (scikit-learn, TensorFlow, PyTorch), Experience with BI tools (Tableau, Power BI), Experience with data warehousing solutions (Snowflake, Redshift, BigQuery)
What You'll Do.
Develop and implement machine learning models
Analyze large datasets to identify trends and insights
Build and maintain data pipelines
Create dashboards and reports for business stakeholders
Collaborate with engineering teams to deploy models
Perform statistical analysis and hypothesis testing
Stay up-to-date with the latest advancements in data
How You'll Work.
Team & Collaboration
Cross-functional teams; Engineering teams; Business stakeholders
Communication Scope
Present findings; Communicate insights
Full Job Description
Purpose of the role – mission The Data Scientist role is responsible for designing and leading universe studies, setting up the extrapolation for Retail Panels, discussing and designing rawdata modelling for non-coop retailers, and supporting root-cause analysis in case of client’s complaints when related to these activities. This role requires strong data fluency, analytical skills with the ability to assess data relevance, and a deep understanding of retail panels and their value. Key activities: 1. Universe Studies: • Design and lead universe studies, acting as a “project manager” for these studies. • Document methodologies for transparency and provide clear explanations of selected methods. • Validate the universe estimation. • Conduct impact analysis of universe studies where relevant. 2. Extrapolation: • Design and simulate extrapolation setups for new panels. • In case of major change in the sample of a channel, redesign its extrapolation matrix, conduct impact analysis where relevant. • Support StatOps team in case of minor events (how to select a donor shop for a copy shop or a create shop, how to slightly update the extrapolation in case of e.g. empty cell, discuss quality issues / risks in case of weak / weaker sample). • Assess the compliance of the existing design of extrapolation matrix and eventually recommend improvement (in the design, the sample size = recommend target for recruitment, etc.) • Specify sample checks per country_channel and ensure that they are properly executed by Operation teams. • Jointly with Operation, review the quality of the existing extrapolation and recommend / redesign if necessary (feedback loop from MDQC teams). • Update the Target Sample File every year and in case of significant change in the universe or the sample. 3. Modelling: • Assess the feasibility of non-coop retailer rawdata modelling outside of the system: business interest (in collaboration with Product), availability of data sources, usability of modelling c
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