Overview In this role you will lead end to end data science initiatives that create competitive advantage across the enterprise. You will own problem framing, model development, deployment, and monitoring, collaborating with cross functional teams to drive growth and operational performance. You'll apply modern ML/AI to deliver production grade solutions and influence senior stakeholders with data driven insights. This is a high visibility, entrepreneurial environment focused on tangible business impact and rapid iteration. Compensation / Benefits4 weeks accrued paid time off9 paid national holidaysTuition reimbursementHealth, dental, and vision insurance from Day 1401(k) employer contribution (up to 7.5%)Paid parental leave ResponsibilitiesFrame high value business problems and translate questions into analytical strategiesDevelop, test, and deploy predictive models that drive growth and performanceApply modern ML/AI techniques to accelerate development and capabilitiesBuild production grade data pipelines, feature engineering, and KPI driven measurement and monitoringProvide clear, impactful storytelling with data to technical and non technical stakeholdersMentor and shape technical direction; share reusable components and elevate engineering standardsOwn solutions from data wrangling to deployment and ongoing monitoring in production Key requirementsPhD in a quantitative fieldStrong foundation in statistics and applied modelingHands on experience with Python and/or R for statistical modelingSQL for large scale data transformation and analysisExperience with GLMs, tree based methods (H2O, XGBoost, LightGBM); familiarity with clustering, Bayesian methods, regularizationExperience shipping models into production with monitoring and iteration (senior/lead level)Strong communication skills; ability to explain methods and tradeoffs to diverse audiencesExperience with cloud and modern data platforms (AWS: S3, EC2, SageMaker; Snowflake)Relational databases expertise; large, multi source datasetsGit/version controlled workflows and good documentation practicesInsurance industry experience is helpful but not requiredstrong communicationproblem framing and strategic thinkingcollaboration with cross functional teamsPython (strongly preferred)RSQL
Not specified in the original listing.
Not specified in the original listing.
