NielsenIQ
Consumer Intelligence
SeniorDataScientist–ClientAnalytics
Neural analysis suggests this role is
optimal for mid candidates.
“Senior Data Scientist – Client Analytics at NielsenIQ. Skills: Python, SQL, Data analysis. Own analytical workstreams. Address business questions”
What You'll Achieve.
Provide data-driven solutions; Provide actionable solutions; Provide timely solutions; Provide innovative solutions; Deliver accurate insights; Deliver timely insights
Industry & Context.
Problem-solving skills; Analytical rigor; Solve business problems; Solve client problems
What They're Looking For.
Must Have
Bachelor’s or Master’s degree in a quantitative discipline, 3–5 years of professional experience in a quantitative or analytics‑driven role, quantitative aptitude, proven data analysis and problem‑solving skills, Python proficiency, SQL proficiency, Experience working with large, complex datasets, Solid understanding of analytical processes and methodologies, verbal skills, written skills, presentation skills, Effective planning and organizational skills, Ability to work independently, Ability to work collaboratively in a team environment, Demonstrated curiosity, commitment to continuous learning
What You'll Do.
Own analytical workstreams
Address business questions
Address client questions
Manage multiple projects
Balance multiple projects
Meet quality standards
Meet accuracy standards
Meet delivery standards
Represent Data Science
Support end-to-end implementation
Evaluate existing methodologies
Evaluate existing processes
Document recommendations
Detect data anomalies
Detect system anomalies
Detect analytical anomalies
Explain technical concepts
Develop technical expertise
Develop analytical expertise
Develop domain expertise
How You'll Work.
Team & Collaboration
Cross-functional interactions; Project discussions; Work collaboratively in a team environment
Communication Scope
Clear communication; Communicate with technical partners; Communicate with non-technical partners; Explain technical concepts; Explain results; Presentation skills; Verbal skills; Written skills
Process & Methodology
Manage multiple projects, Balance multiple projects, Effective planning skills, Organizational skills
Full Job Description
R26_0004637 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. This Senior Data Scientist – Client Analytics position was created to support our US Data Science initiatives. This position requires someone who is passionate about learning, multi sourced data, data quality, statistical measurement, automation, efficiency, and consumer behavior. About You You are a disciplined, curious, and self‑starting data scientist who values trusted data, analytical rigor, and clear execution. You proactively take ownership of your analyses, work effectively with complex, multi‑sourced data, and apply sound statistical and analytical methods to solve well‑defined business and client problems. You care deeply about data quality, documentation, and deliver accurate, timely insights. You communicate clearly with both technical and non‑technical partners, collaborate effectively across teams, and are motivated to continuously deepen your expertise in NIQ data assets, measurement concepts, and scalable analytics. Responsibilities: * Own detailed analytical workstreams to address defined business and client questions. * Manage and balance multiple projects while meeting quality, accuracy, and delivery standards. * Represent Data Science in cross‑functional interactions and project discussions. * Support end‑to‑end implementation of analytical solutions, from analysis through delivery. * Evaluate existing methodologies and processes to ensure appropriate application. * Document analyses, findings, assumptions, and recommendations clearly and concisely. * Detect, troubleshoot, and resolve data, system, or analytical anomalies within assigned work. * Explain technical concepts and results to teams
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