Data Scientist - Modeling and Analytics

Role overview

Responsibilities

• check_circle 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.
• check_circle Conduct advanced exploratory data analysis. Perform model interpretability and explainability analysis.
• check_circle 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
• check_circle Apply data privacy and compliance rules under regulations like GDPR, CCPA. Apply ethical AI principles. Apply model fairness and bias mitigation techniques.
• check_circle Conduct analyses to assess model performance and campaign performance, both against test datasets and actual results once deployed.
• check_circle Forecast campaign results based on models built and validate forecast against actuals.
• check_circle Work with marketing data architects and engineers to ensure data is clean, complete, correct, and suitable for modeling using AI/ML platforms.
• check_circle Develop and maintain data pipelines. Implement feature engineering techniques. Find, recommend, and purchase additional data to use in model building
• check_circle Proactively identify opportunities for model improvement and need for additional modeling projects.
• check_circle Maintain clear and organized documentation of data, methodologies, and results.
• check_circle Implement automation in existing processes to improve overall efficiency.
• check_circle Perform ad hoc analysis to support Marketing Distribution efforts
• check_circle Actively seek out innovation and optimization use cases and experiments that will result in organizational transformation and sales and profit improvements.
• check_circle Skilled in cross-functional collaboration, agile methodologies, project management and stakeholder communication.
• check_circle Advanced training or academic focus in non-parametric statistics, resampling methods, or Bayesian approaches for small sample inference
• check_circle Experience applying sequential testing or multi-armed bandit approaches to maximize insights from limited samples in marketing contexts
• check_circle Able to effectively communicate and translate complex, technical finding in a candid, clear, concise, and non-technical fashion to all audiences
• check_circle Maintain perspective between the big picture and the tactical details. Remains aligned with the organization’s strategic plan.
• check_circle 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.
• check_circle Self-starter with the ability to identify priorities and focus on items with high business impact.
• check_circle Ability to present complex analytical findings with persuasiveness and succinctness.

Preferred qualifications

• Master’s degree in Statistics, Economics, Mathematics, Data Science, or related field. Experienced in marketing analytics or customer behavior modeling.
• 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