Data Modeling and Machine learning – Developing response, premium, risk and other predictive models using advanced Statistical and Machine Learning techniques to limit solicitation with most profitability for direct mail, digital and telemarketing campaigns. Technologies used – R, Python, SAS, H2O.ai, Microsoft Azure ML. Sampling research – Researching and formulating all sampling methodologies for different types of campaign needs. From systematic combined sampling for generic models to stratified and cluster-based sampling for client specific models. Model validations – Tracking marketing campaigns performance through scheduled and triggered production models validations, model KPIs evaluations, head-to-head significance tests and other validation comparisons. Trend Analytics – Developing processes and dashboarding to identify and pick latest customer trends and patterns from customer engagement, customer behavioral and marketing data to optimize the effectiveness of future customer engagement strategies. Technologies used – R, Shiny, Python, Tableau, SQL. Test designs (A/B testing) – Optimizing campaign parameters in marketing programs by testing attributes such as insurance premium, coverage amount, creative elements, etc. to improve marketing efficiency and profitability. Marketing campaigns architecture – Providing statistical leadership to internal and external partners and formulating decision management plans that include project planning, data collection, data flow, data analysis and modeling, campaigns execution, and results tracking. Business Decision Modeling – Modeling quarterly and annual drop volumes in planning, budgeting, and allocations to align as per evolving business goals and strategy. Data Analytics – Managing and tracking Client’s customer demographics and marketing profile by conducting detailed profile analysis based on their demographics, behavior, history – Track files, measure exhaustion, tap changing trends to keep campaign KPIs on track and provide actionable business decisions and recommendations. Data Testing – Testing vast amount of internal and external data sources and data vendors as potential candidates to add or replace existing data sources/vendors to enhance marketing, risk and finance models. Technical advancements – Diversifying and enhancing operational and development technologies incorporating newer AI platforms, more open-source and cloud computing and expanding ETL framework. Machine Learning research – Constantly developing and testing new Machine Learning techniques and algorithms against control models to uplift marketing efficiency and bridge marketing exhaustion gaps; Model Governance – Review and report all new models with peers to incorporate any feedback. Collaborate with peers to provide them any feedback and recommendations on their model builds before publishing into production. Reporting – Developing Client reports, internal modeling reports, analyses reports, for peer-review and in-house technical manuals; Leads the modeling team by ensuring there is a balanced distribution of work across team members, remaining up to date with the team’s responsibilities/progress on projects, bringing in new tools and techniques as needed and providing training to enhance team’s technical skills. 40hrs wk / $135,200/yr..

Train new hires in all aspects of their job. Assess model performance and model validation results from the team and make recommendation to improve results based on data and technology. Professionally and effectively represent Modeling in all client facing situations, as well as to all levels of senior management.  Act as a resource for the other managers and analysts in the department, as well as to Directors and Programming staff, to assist in issue resolution, process improvements, and education and training as necessary. 

Must have a Master’s degree in Statistics, Business Analytics or related field plus four (4) years of relevant experience. Experience must also include four (4) years of experience utilizing the follow tools/techniques: Machine Learning, Statistics, Predictive Modeling, ETL, Data Mining Machine Learning, Statistics, Predictive Modeling, ETL and Data Mining; Four (4) years of experience utilizing any one or a combination of the following programs: R, Python or SAS; Four (4) years with data warehouse and SQL; Two (2) years of experience utilizing H20.ai; May work from home within commuting distance of the office.  Monday through Friday. 40 hrs./wk; $135,200/yr. Must have proof of legal authority to work in the U.S.