Marketing Data Analyst

PEGA Data Science & Analytics – Marketing Analytics Position Summary We are seeking a Data Science & Analytics professional with strong PEGA Customer Decision Hub (CDH) expertise and a solid understanding of marketing analytics to support enterprise customer decisioning initiatives. The ideal candidate will understand PEGA from a marketing perspective and be able to analyze, manipulate, and interpret large datasets to generate meaningful marketing insights from predictive models. This role will focus on enabling faster implementation of new CDH modeling features by developing reusable analytical assets, standardizing data retrieval processes, and building scalable analytics solutions using Databricks, Python/PySpark, and SQL. Experience with customer behavior analytics, Next Best Action (NBA), and marketing performance measurement is highly preferred. Key Responsibilities • Analyze PEGA CDH customer decisioning data to generate actionable marketing insights and improve campaign effectiveness. • Leverage marketing and customer interaction data to evaluate predictive models and identify optimization opportunities. • Develop reusable SQL queries and PySpark scripts to replicate PEGA CDH customer contextual objects within Databricks and other analytical environments. • Standardize analytical processes for customer interaction, model performance, and member profile analysis. • Build reusable Databricks notebooks supporting: • Distribution Analysis • Arbitration Analysis • Channel Engagement Analysis • Perform back-testing, establish baseline KPIs, and develop monitoring frameworks for new CDH modeling features. • Analyze model maturity and recommend enhancements based on marketing performance and customer engagement data. • Monitor predictive model performance and improve analytical logic supporting enterprise marketing initiatives. • Develop monitoring capabilities to identify low propensity scores, ineffective marketing actions, and customer engagement opportunities. • Build eligible audience monitoring frameworks to identify target customer segments and treatment opportunities. • Collaborate with Marketing, Data Science, Analytics, and Engineering teams to translate business questions into scalable analytical solutions. Required Qualifications • Strong experience with Databricks. • Advanced proficiency in Python, PySpark, and SQL. • Hands-on experience with PEGA Customer Decision Hub (CDH). • Strong understanding of PEGA from a marketing perspective, including customer decisioning, campaign optimization, and marketing analytics. • Experience manipulating and analyzing large datasets to generate marketing insights from predictive models. • Experience building reusable analytical notebooks and data pipelines. • Strong knowledge of predictive analytics, KPI development, model monitoring, and customer analytics. • Excellent analytical, problem-solving, and communication skills. Preferred Qualifications • Experience with Next Best Action (NBA) and Customer Decisioning platforms. • Experience in Marketing Analytics, Customer Journey Analytics, and Campaign Performance Analysis. • Knowledge of propensity modeling, arbitration strategies, and customer engagement optimization. • Experience developing executive dashboards and model health reporting. • Experience supporting enterprise-scale marketing analytics initiatives. Technical Skills • PEGA Customer Decision Hub (CDH) • Marketing Analytics • Customer Decisioning • Next Best Action (NBA) • Databricks • Python • PySpark • SQL • Predictive Analytics • Customer Analytics • Model Performance Monitoring • KPI Development • Data Visualization • Marketing Insights • Campaign Analytics • Customer Segmentation • Data Engineering Key Deliverables • Standardized SQL queries and PySpark scripts for PEGA CDH analytics. • Reusable Databricks notebooks for enterprise marketing analytics. • Customer interaction, model performance, and campaign effectiveness analysis. • Predictive model monitoring and KPI dashboards. • Marketing insight reports supporting customer engagement and acquisition strategies. • Eligible audience monitoring frameworks and customer segmentation analysis. • Actionable analytics to improve campaign performance, customer engagement, and enterprise decisioning capabilities.