Senior Data Scientist (NLP & GenAI)

Kratos Growth operates a consumer intelligence platform that transforms billions of data points into strategic insights for global brands such as Coca Cola, Unilever, and Bayer.

• 2-4+ years in data science/machine learning roles with production model development
• 1-2+ years focused primarily on NLP tasks such as sentiment analysis, classification, or topic modeling
• 1+ years working with LLMs including prompt engineering, fine-tuning, or RAG implementations
• At least one model deployed to production or demonstrably used to inform business decisions
• 2-3+ years production experience with Python in ML environments
• Proficiency with ML/NLP libraries: scikit-learn, Hugging Face Transformers, SpaCy, NLTK
• Intermediate SQL skills including JOINs, aggregations, and window functions on datasets over 1 million rows
• Experience with Git-based version control workflows including code reviews and collaborative development
• Bachelor's degree in Computer Science, Statistics, Mathematics, or related quantitative field

Nice to have:

• Experience with Azure cloud platform and/or Databricks
• Experience with LangChain or similar LLM orchestration frameworks
• Experience with PySpark or distributed computing
• Multi-language NLP experience beyond English
• Background in consumer insights, CPG, or marketing analytics
• Specific experience developing emotion detection models
• MLOps experience including model monitoring, A/B testing, and automated retraining

• Develop and deploy NLP models for sentiment analysis, emotion detection, and text classification processing over 1 million documents daily
• Design and implement LLM-based solutions using prompt engineering, RAG architectures, and fine-tuning approaches
• Build evaluation frameworks to measure model accuracy, establish baselines, and track improvements
• Collaborate with data engineers and AI engineers to productionize models via API endpoints and batch pipelines
• Work with multi-language text data (English primary, with global market expansion)
• Translate business requirements into technical model specifications
• Document model architectures, performance metrics, and usage guidelines for cross-functional teams
• Iterate on models based on client feedback and production performance data

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