Senior Scientist (NLP & GenAI) - Consumer Insights, Brand Equity & Trends

Our consumer intelligence platform transforms billions of data points into strategic insights for global brands such as Coca Cola, Unilever, and Bayer.

• 5+ years in data science, analytics, or quantitative research with at least 2 years in consumer insights at CPG/FMCG companies, research agencies, or consumer data/social listening firms.
• Deep understanding of brand equity measurement frameworks and how they link to share and sales.
• Experience building category frameworks or taxonomies including benefits, attributes, need states, ingredients, or formats.
• Hands-on expertise with NLP on unstructured consumer text including classification, topic modeling, LLM-based classification, and prompt design.
• Strong statistical skills for validation and diagnosis: time series, lead-lag and correlation analysis, seasonality adjustment, driver analysis.
• Proficient in Python, SQL, with experience in PySpark or Databricks.
• Degree in Statistics, Mathematics, Computer Science, Econometrics, Marketing Science, Psychology, or related quantitative field; master's or PhD is a plus.

Nice to have:

• Trend forecasting from search and social data.
• Experience with survey-based brand trackers or syndicated market data (e.g., Kantar, NielsenIQ, Circana).
• Experience beyond CPG such as beauty, consumer health, retail, sports, or media.
• Marketing mix modeling or causal inference for marketing.
• Fluency in multiple languages.
• Publications, talks, or awards in market research (e.g., MRS, ESOMAR, Quirk's).

• Design and maintain category taxonomies (benefits, ingredients, formats, need states) across categories, markets, and new industries such as sports.
• Build classifiers that tag consumer text against taxonomies in 20+ languages, measuring their precision and recall.
• Build and validate product equity measures against share, sales, and brand trackers, separating real change from seasonality.
• Back-test Trend Spotter's Future Ranking Factor against market data and refine the separation of fads from lasting trends.
• Translate model output into clear stories and validation studies for clients.
• Coach other data scientists on insights methods.

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