Lead Data Scientist - IntelliScript

• What this position entails
• What you will be doing
• Research, develop, deploy, and maintain traditional AI/ML models following industry best practices
• Coordinate with Product, Business Development, ML Engineering, and IT to bring new and exciting data science products to market, as well as support existing industry leading products
• Help drive best practices and continuous improvement on the data science team; influencing model design and experimentation strategy through planning, audit, peer review, and other coaching
• What we need
• 10+ years of professional experience using AI/ML to create high return on investment commercial data science solutions
• Expertise with Electronic Health Records or unstructured data analysis
• Expert data scientist with demonstrable capability building traditional AI/ML models
• Supervised Learning: Linear/logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM, CatBoost), and ensemble methods
• Unsupervised Learning: K-means clustering, hierarchical clustering, PCA, and anomaly detection algorithms
• Model Validation: Cross-validation strategies, hyperparameter optimization (Grid Search, Random Search, Bayesian optimization), and A/B testing frameworks
• Deep Learning Architectures: Neural networks, transformers, and transfer learning methodologies
• NLP Algorithms: Text preprocessing, TF-IDF, word embeddings (Word2Vec, GloVe), topic modeling (LDA), sentiment analysis, and named entity recognition
• Expert understanding of NLP and generative AI; able to effectively use, fine-tune, and evaluate commercially available models as well as deploy and integrate local LLMs into the data science process
• Hands-on experience building GenAI applications (e.g., RAG systems, LLM evaluation frameworks, or GenAI-powered internal tools)
• Expert level Python programmer, with some experience in R and/or SQL
• Expert user of Databricks or similar cloud-based model development ecosystem including mlflow, experimentation organization, data catalogs, and compute cluster configuration
• Sufficient understanding of software engineering best practices such as Git for version control, unit testing, local development, and environment management
• Knowledge of ML Engineering and ML Ops related concepts and tools including CICD pipelines, GitHub Actions, Docker, AWS Lambda, and Linux
• Degree in a relevant field (computer science, data science, statistics, mathematics, applied math, actuarial science, economics, etc.)
• What you bring to the table
• Excellent communication skills, in person and through phone / email
• Proven ability to understand client analytical needs, translate them into an action plan, then execute and deliver

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