Nielsen is seeking a strategic and analytical Senior Data Scientist / Senior Decision Scientist to lead the technical execution and analysis of our Customer Experience research. In this role, you will lead the analytical strategy of our flagship semi-annual Client Satisfaction Survey - informing the design to ensure data quality and leading the deep-dive analysis that follows and more broadly you will serve as the bridge between raw data and business strategy. We are looking for a candidate who can apply rigorous research methods to inform survey design, integrate that feedback with internal product and usage data, and build predictive models to inform decision-making for Nielsen’s senior leadership.

Responsibilities

Predictive Modeling & Advanced Analysis

• Driver Analysis: Build statistical models to identify the key drivers of Customer Experience metrics (NPS, CSAT, CES). You will determine which specific operational factors (e.g., product speed, support response time) have the highest impact on client sentiment.

• Client Health Modeling: Design and build a comprehensive "Client Health" model. You will determine the weighting of various inputs (survey scores, product usage, support tickets) to create a robust picture of account health across our Product portfolio.

• Retention & Dissatisfaction Analysis: Develop statistical models to predict retention risks. You will analyze historical data to identify early warning signs of dissatisfaction and link them to specific operational or product factors.

• Behavioral Linkage: Go beyond isolated survey scores by quantifying the relationship between what clients do (telemetry/usage) and how they feel (sentiment), creating a holistic view of the Customer Experience.

Survey Strategy & Design Influence

• Design Strategy: Partner with the survey operations team to inform the questionnaire design. You will ensure the instrument captures the specific predictive signals and psychographic variables needed to power your retention and health models.

• Statistical Rigor: Advise on sampling frames and statistical weighting methodologies. You will provide the technical oversight to ensure the data collected is accurate and representative of Nielsen’s diverse client base.

• Instrument Optimization: Review and recommend evolutions to the survey instrument, ensuring it allows for year-over-year trend analysis while adapting to new modeling requirements.

Insights Management & Presentation

• Executive Reporting: Translate complex models and datasets into clear, visual executive summaries. You should feel comfortable presenting results to all levels of leadership when required.

• Stakeholder Engagement: Collaborate with Commercial, Product, and Tech teams to help them understand the data. You will be the "go-to" expert when leaders have questions about the methodology or specific findings.

• Actionable Recommendations: Move beyond reporting the numbers by providing data-backed recommendations on where the business should focus to improve the Customer Experience.

Required (Must-Haves) - 5 -10 yrs - relevant skils

• Education: Bachelor’s or Master’s degree in Statistics, Data Science, Market Research, Psychology, or a related quantitative field.

• CX Metrics Knowledge: Experience working with standard CX frameworks such as NPS (Net Promoter Score), CSAT (Customer Satisfaction), and CES (Customer Effort Score).

• Modeling Experience: Proven ability to build statistical models (e.g., Regression, Key Driver Analysis, Churn Prediction) within the context of Customer Experience / Customer Success to solve business problems. You must be able to explain why a metric moved, not just report that it did.

• Research Expertise: Strong experience in quantitative survey research, specifically in designing for analysis. You understand how question types and scales impact your ability to model the data later.

• Communication: You must be comfortable simplifying complex data for non-technical senior audiences and answering questions about your findings.

• Strategic Thinking: Ability to interpret data within the context of the wider business strategy.

• SQL & Data Manipulation: Proficiency in SQL is required for handling larger datasets, joining multiple data sources (Survey + Product Data), and leveraging our data lakehouse in Databricks.

Desired (Nice-to-Haves)

• Cloud Data Warehouses: Experience working with Databricks is a strong plus.

• Warehouse ML and Agentic Design: Familiarity with performing machine learning or statistical modeling directly within a warehouse platform (e.g., Databricks ML, Snowflake, BigQuery ML) and leveraging agentic tooling for automation and speed.

• Visualization Tools: Familiarity with BI tools (e.g., Tableau, PowerBI).

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