Lead Data Scientist

<p><strong>Job Summary:</strong></p><p>We’re looking for a senior data science leader with <strong>10+ years of professional AI/ML experience</strong> and strong hands-on expertise in both traditional ML and <strong>Generative AI/NLP</strong>.</p><p><br></p><p><strong>Must have: </strong>10yrs in Data Science, EHR & PhD (Education)</p><p><br></p><p><br></p><p><strong>Key Requirements:</strong></p><ul><li>10+ years building <strong>commercial AI/ML solutions</strong> with measurable business impact</li><li>Expert-level <strong>Python</strong>; experience with <strong>R and/or SQL</strong></li><li>Strong expertise in traditional ML, including <strong>regression, decision trees, Random Forest, XGBoost/LightGBM/CatBoost, ensemble methods, clustering, PCA, and anomaly detection</strong></li><li>Strong knowledge of <strong>model validation, cross-validation, hyperparameter optimization, and A/B testing</strong></li><li>Hands-on experience with <strong>deep learning, neural networks, transformers, and transfer learning</strong></li><li>Expert knowledge of <strong>NLP</strong>, including text preprocessing, TF-IDF, embeddings, topic modeling, sentiment analysis, and NER</li><li>Strong <strong>Generative AI/LLM</strong> expertise, including model evaluation, fine-tuning, and integrating local/commercial LLMs</li><li>Hands-on experience building <strong>GenAI applications such as RAG systems, LLM evaluation frameworks, or internal GenAI tools</strong></li><li>Expertise with <strong>Databricks or a similar cloud-based ML ecosystem</strong>, including MLflow, experimentation, data catalogs, and compute configuration</li><li>Knowledge of <strong>ML Engineering/MLOps</strong>, including CI/CD, GitHub Actions, Docker, AWS Lambda, and Linux</li><li>Strong software engineering practices including <strong>Git, unit testing, local development, and environment management</strong></li><li>Experience working with <strong>Electronic Health Records (EHR) and/or unstructured data</strong> is highly valuable</li><li>Ability to partner with <strong>Product, Business Development, ML Engineering, and IT</strong> teams and translate business needs into scalable data science solutions</li><li>Relevant degree in <strong>Computer Science, Data Science, Statistics, Mathematics, Actuarial Science, Economics</strong>, or a related field</li></ul><p>.</p>

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