REMOTE Lead Data Scientist

Required Skills & Experience • \t10+ years of professional experience using AI/ML to create high return on investment commercial data science solutions\n•\tExpertise with Electronic Health Records or unstructured data analysis\n•\tExpert data scientist with demonstrable capability building traditional AI/ML models\no\tSupervised Learning: Linear/logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM, CatBoost), and ensemble methods\no\tUnsupervised Learning: K-means clustering, hierarchical clustering, PCA, and anomaly detection algorithms\no\tModel Validation: Cross-validation strategies, hyperparameter optimization (Grid Search, Random Search, Bayesian optimization), and A/B testing frameworks\no\tDeep Learning Architectures: Neural networks, transformers, and transfer learning methodologies\no\tNLP Algorithms: Text preprocessing, TF-IDF, word embeddings (Word2Vec, GloVe), topic modeling (LDA), sentiment analysis, and named entity recognition\n•\tExpert 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\n•\tHands-on experience building GenAI applications (e.g., RAG systems, LLM evaluation frameworks, or GenAI-powered internal tools)\n•\tExpert level Python programmer, with some experience in R and/or SQL\n•\tExpert user of Databricks or similar cloud-based model development ecosystem including mlflow, experimentation organization, data catalogs, and compute cluster configuration\n•\tSufficient understanding of software engineering best practices such as Git for version control, unit testing, local development, and environment management\n•\tKnowledge of ML Engineering and ML Ops related concepts and tools including CICD pipelines, GitHub Actions, Docker, AWS Lambda, and Linux\n•\tDegree in a relevant field (computer science, data science, statistics, mathematics, applied math, actuarial science, economics, etc.) Nice to Have Skills & Experience • \tPhD in relevant field or Actuarial designation (FCAS/FSA)\n•\tExperience in one of the following industries: healthcare, insurance (L&H or P&C), finance, life sciences, or similar fields\n•\tExperience at an InsurTech or FinTech\n•\tPast experience working in a HIPAA / PHI / PCI compliant environment Job Description Insight Global is seeking a Lead Data Scientist for our customer that provides data, products, and professional services to the insurance industry. The Lead Data Scientist will be integral as we continue to bring innovative new products to market in response to customer needs and emerging business opportunities. This individual will leverage expertise in data science, machine learning, Generative AI (GenAI), and Natural Language Processing (NLP) to develop a deep understanding of existing product capabilities and drive the creation of advanced analytical solutions. Projects will include the design, validation, documentation, and deployment of sophisticated AI and machine learning models that address complex challenges across healthcare, insurance, life sciences, and related industries. The primary responsibility of the Lead Data Scientist is to partner with business development, product, and engineering teams to develop and implement commercially viable, data-driven solutions that support a variety of market segments. Responsibilities include:\n•\tResearch, develop, deploy, and maintain traditional AI and machine learning models following industry best practices.\n•\tUtilize and evaluate Generative AI technologies to build innovative solutions for both internal and external business use cases.\n•\tAdvance organizational AI capabilities through centralized tooling, experimentation, and technical thought leadership.\n•\tCollaborate closely with Product, Business Development, Machine Learning Engineering, and IT teams to bring new data science products and capabilities to market.\n•\tSupport and enhance existing industry-leading products through advanced analytics and model development.\n•\tDrive best practices and continuous improvement across the data science function, including model design, experimentation strategy, validation, peer review, and technical mentorship.\n•\tTranslate complex business challenges into scalable analytical solutions that deliver measurable business value.\n•\tStay current on emerging trends in AI, machine learning, NLP, and GenAI to identify opportunities for innovation and competitive differentiation. Insight Global