Data Scientist

Data ScientistLead use case/workstream with junior data scientistsContribute to the end-to-end model lifecycle, including data exploration and understanding, feature engineering, model training and validation, ensuring quality, security, scalability, and fairnessSupport use case development that includes initial project scoping, project/sample design, reception and processing of data, performing analysis and modeling to creation of final report/presentationData wrangling/data matching/ETL to explore a variety of data sources, gain data expertise, perform summary analyses and prepare modeling datasetsUtilizing advanced statistical and AI/ML techniques to create high-performing predictive models and creative analyses to address business objectives and partner needsIdentification of source data and data quality checks both in model/solution development and in productionPackaging of model/solution and deployment in cooperation with Data Engineers and MLOpsImplement new statistical or other mathematical methodologies as needed for specific models or analysisPropose innovative ways to look at problems through using data mining and data visualizationWork with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutionsPresent information using data visualization techniques; communicate results and ideas to key decision makersEnsure data accuracy and consistent reporting by performing regular data quality control, prepare and maintain reports, and troubleshoot data anomaliesAdhere to model governance, documentation, testing, and other best practices in partnership with key stakeholdersPhD with 2+ years of experience, Master's degree with 4+ years of experience in Statistics, Computer Science, Engineering, Applied mathematics or related field3+ years of hands-on ML modeling/development experienceBackground in insurance and underwriting preferredSolid understanding of data analysis and statistical modelingKnowledge of a variety of machine learning techniques (clustering, decision tree, bagging/boosting artificial neural networks, etc.) and their real-world advantages/drawbacksDemonstrated track records in experimental design and executionsHands-on experience with data wrangling including fuzzy matching and regular expression, distributed computing and applying parallelism to ML solutionsStrong programming skills in PythonSolid background in algorithms and a range of ML modelsExcellent communication skills and ability to work and collaborate cross-functionally with Product, Engineering, and other disciplines at both the leadership and hands-on levelExcellent analytical and problem-solving abilities with superb attention to detailProven experience in providing technical leadership and mentoring to data scientists and strong project management skills with ability to monitor/track performance for enterprise successExperience communicating complex ideas simply, presenting impact, trade-offs, and recommendations to non-technical partnersWorking knowledge of core software engineering concepts (version control with Git/GitHub, testing, logging,...)Working knowledge of NLP, LLMs, RAG architecture, and agent frameworks, including safe automation design and evaluation systems