Data Scientist - Modeling and Analytics

Responsibilities As a Data Scientist – Modeling and Analytics, you will be responsible for creating statistical models and performing analyses that drive sales and policy growth for the organization. You will partner with marketing team members, marketing managers, and other data analysts and scientists to identify business needs, gather data, build, and maintain effective models, and assess model performance over time. Build, maintain, and automate models to predict purchase propensity, policy premium, policy lapse/retention, cross‑selling, upselling, next best action, and other consumer behaviors using both internal data, census data, appended aggregated data, and macroeconomic data. Recommend marketing distribution strategies leveraging data and models. Conduct advanced exploratory data analysis. Perform model interpretability and explainability analysis. Leverage specific metrics for model performance evaluation (e.g., precision, recall, F1 score). Implement A/B testing and experimental design and quantitative benchmarks for model improvement. Apply data privacy and compliance rules under regulations like GDPR, CCPA. Apply ethical AI principles. Apply model fairness and bias mitigation techniques. Conduct analyses to assess model performance and campaign performance, both against test datasets and actual results once deployed. Forecast campaign results based on models built and validate forecast against actuals. Work with marketing data architects and engineers to ensure data is clean, complete, correct, and suitable for modeling using AI/ML platforms. Develop and maintain data pipelines. Implement feature engineering techniques. Find, recommend, and purchase additional data to use in model building. Proactively identify opportunities for model improvement and need for additional modeling projects. Maintain clear and organized documentation of data, methodologies, and results. Implement automation in existing processes to improve overall efficiency. Perform ad hoc analysis to support Marketing Distribution efforts. Actively seek out innovation and optimization use cases and experiments that will result in organizational transformation and sales and profit improvements. Requirements Skilled in cross‑functional collaboration, agile methodologies, project management and stakeholder communication. Advanced training or academic focus in non‑parametric statistics, resampling methods, or Bayesian approaches for small sample inference. Experience applying sequential testing or multi‑armed bandit approaches to maximize insights from limited samples in marketing contexts. Able to effectively communicate and translate complex, technical findings in a candid, clear, concise, and non‑technical fashion to all audiences. Maintain perspective between the big picture and the tactical details. Stellar attention to detail, including maintaining accuracy and consistency across a suite of data science assets, keeping documentation up to date, and proactively identifying and addressing any quality concerns. Self‑starter with the ability to identify priorities and focus on items with high business impact. Ability to present complex analytical findings with persuasiveness and succinctness. Master’s degree in Statistics, Economics, Mathematics, Data Science, or related field. 5 to 7 years of experience in data science, including hands‑on experience with Machine Learning (e.g., scikit‑learn, TensorFlow, PyTorch, DataRobot, Databricks) and Generative Artificial Intelligence. Experience with automated model deployment and monitoring tools. Possess outstanding analytical, modeling, problem‑solving, and critical‑thinking skills. Experienced with cloud platforms such as AWS, Azure, and Google Cloud. Familiar with big data technologies (Spark, Hadoop). Strong knowledge of machine learning algorithms and their applications in automated systems. Experience with advanced modeling techniques like ensemble methods, time series analysis, and probabilistic modeling. High proficiency in Python or R for statistical analysis, model development, and process automation. Proficient with SQL for data extraction and manipulation. Proficiency with data visualization tools (Power BI, Tableau, or similar) and their automation capabilities. Hard Skills Statistical models Data analysis Machine learning Feature engineering A/B testing Model interpretability Data pipelines Automated model deployment Data visualization Non‑parametric statistics Soft Skills Cross‑functional collaboration Project management Stakeholder communication Attention to detail Self‑starter Analytical skills Problem‑solving Critical thinking Effective communication Innovation Certifications & Qualifications Master's degree in Statistics Master's degree in Economics Master's degree in Mathematics Master's degree in Data Science #J-18808-Ljbffr Jobtailor