NielsenIQ
Consumer Intelligence
LeadDataScientist
Neural analysis suggests this role is
optimal for mid candidates.
“Lead Data Scientist at NielsenIQ. Skills: Python, Statistics, Data Science, Cloud computing. Develop innovative solutions. Uncover consumer journey”
What You'll Achieve.
Make bold decisions; Transform businesses; Drive innovation and growth; Provide data-driven, actionable, timely, and innovative solutions; Uncover the complete consumer journey; Revolutionize the way we measure consumer behavior; Make a significant impact in consumer analytics
Industry & Context.
Solving complex client challenges; Data-driven, actionable, timely, and innovative solutions; Statistical and data analytical methods; Data Analysis Skills; Statistical Expertise
What They're Looking For.
Must Have
Master’s / Doctorate Degree in Data Science, Mathematics, Statistics or BETech, Computer Engineering degree in Computer Science, Data Science or related fields involving statistical analysis of large data sets, Programming in Python, Statistical methods, Data analysis skills, Experience presenting findings
Nice to Have
Doctorate Degree, Experience with (un)managed crowdsourced panels, Experience with receipt capture methodologies, Experience programming efficiently to process large amounts of data, Experience in SQL, Working with queries, Statistical and logical skills, Experience in data cleaning, Outlier validation, Sampling, Bias reduction, Indirect estimation, Data aggregation techniques, Knowledge in software engineering, Experience designing and developing software, Familiarity with technology stacks for cloud computing, Experience with version control systems GitHub or Bitbucket
What You'll Do.
Develop innovative solutions
Uncover consumer journey
Develop tool to simulate impact
Drive innovation with efficiency
Design and develop software
Apply statistical methods
Handle complex data sets
Manipulate large data sets
Analyze large data sets
Interpret large data sets
Present findings and recommendations
Process large amounts of data
Support cross-functional stakeholders
Transform panel measurement business
How You'll Work.
Team & Collaboration
Supporting cross-functional stakeholders in production deployment
Communication Scope
Communication; Writing; Collaboration skills
Full Job Description
Our NielsenIQ teams empower our clients to make bold decisions and transform their businesses in trusted data, solutions, and insights designed to drive innovation and growth. The NielsenIQ North America Data Science team is passionate about solving complex client challenges and providing data-driven, actionable, timely, and innovative solutions. Join a dynamic and diverse global team dedicated to developing innovative solutions that uncover the complete consumer journey for our clients. We are seeking a highly skilled Data Scientist with strong development skills in programming languages such as Python. Additionally, expertise in statistics, mathematics, econometrics, and experience with panel data to revolutionize the way we measure consumer behavior both online and in-store. Looking ahead, we are excited to find someone who will join our team in developing a tool that can simulate the impact of production process changes on client data. This tool outside of the production factory will allow the wider Data Science team to drive innovation with unpresented efficiency. ## Qualifications About You Ideally you possess a good understanding of consumer behavior, panel-based projections, and consumer metrics and analytics. You have successfully designed and developed software applying statistical and data analytical methods and demonstrated your ability to handle complex data sets. Experience with (un)managed crowdsourced panels and receipt capture methodologies is an advantage. - Educational Background: Master’s / Doctorate Degree in Data Science, Mathematics, Statistics or BE/BTech. Computer Engineering degree in Computer Science, Data Science or related fields involving statistical analysis of large data sets - End-to-End Mindset: Experienced understanding processes also holistically. You enjoy connecting methodology, engineering, and business context to build solutions that work end‑to‑end—spanning data ingestion, modeling, tooling, deployment, and impact evaluation.
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