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Kainoa Eastlack
Senior Data Scientist specializing in NLP and explainable machine learning
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About
NLP/ML practitioner who built an explainable, clinician-aligned system to detect cognitive decline (Alzheimer’s/stroke-related) from audio responses, achieving 97% accuracy on only a few hundred data points. Also has experience with healthcare claims entity resolution and prototyped a word2vec-based patent search vector database in Elasticsearch, with strong emphasis on testing, interpretability, and scalable Python data workflows.
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Key Strengths
- Built explainable cognitive-decline detection system from audio with 97% accuracy (beat human experts)
- Strong feature engineering grounded in clinician-defined diagnostic signals (hybrid rules + ML)
- Handles small-data/high-stakes modeling with overfitting-aware model selection (Ridge, k-NN)
- Invented/iterated novel NLP features to handle linguistic edge cases (e.g., multi-word tokens)
- Rigorous validation via extensive unit tests plus real-data case studies
- Entity resolution for messy healthcare claims using source-aware similarity scoring and thresholding
- Production-minded data workflow design for scalability (chunking, orchestration, AWS/EC2 autoscaling)
- Clear stakeholder communication; delivered in 1 month vs 3-month expectation
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