• Design, build, and deploy machine learning solutions for audience targeting, lookalike generation, and individual propensity scoring. • Own the complete ML lifecycle - from exploratory analysis and experimentation all the way through production deployment and operational monitoring. • Develop and ship production ML systems spanning self-supervised representation learning, vector similarity search, and supervised classifiers. • Leverage distributed computing (Spark/Databricks) and cloud data platforms (AWS, Snowflake) to build and run production ML pipelines at scale. • Ensure model quality through rigorous evaluation practices: from embedding validation and retrieval quality to supervised model calibration and production monitoring. • Engineer features at scale from demographic, behavioral, and identity data — including handling missing values, encoding strategies, and pipeline-level data quality validation. • Contribute ML logic directly into shared production services, working alongside data engineering, software engineering, and product teams.

Role Description The Business Intelligence & Analytics (BIA) CoOp will support the BIA team in delivering data-driven insights that enable a well-informed, analytics-led marketing strategy. The CoOp will contribute to the development of advanced dashboards, reporting, and predictive models, as well as perform exploratory data analysis to uncover trends and opportunities. Through this hands-on work, the CoOp will help power marketing campaigns designed to increase donor engagement and revenue for Public Media stations across the United States. This role offers practical experience applying analytics to real-world marketing challenges while collaborating closely with analysts, data scientists, and marketing stakeholders. - Data analysis and report development - Weekly support of CRM data and reporting requests - Predictive model support and development - Data quality reviews and data validation Qualifications - Thorough and detail oriented with excellent follow-up skills - Knowledge of database principles and data science - Able to juggle multiple tasks and competing priorities in a fast-paced environment - Able to follow instructions and work independently when needed - Flexible and can switch gears quickly - A team player with great interpersonal and communication skills Requirements - 0 – 1 years relevant experience in data analysis, predictive modeling and/or data analytics - Foundational understanding of SQL and database design - Proficiency with Microsoft Office Suite: Excel, Word, Outlook, PowerPoint - Excellent written and verbal communication skills - Excellent multitasking, time management and organizational skills - Solid critical thinking and problem-solving skills - Education: Bachelor's degree candidate - Duration: six months, Full Time (32+ hours per week) Company Description CDP, a Boston-based public benefit corporation, uses the collaborative fundraising model to improve the operational efficiency and revenue generation capacity of the public media system. CDP currently provides more than 230 NPR and PBS stations with outsourced marketing, data analytics, fundraising, and technology services. These services help stations raise more net income, grow their donor files faster, lower their fundraising costs, and free up development talent allowing stations to better focus on local engagement. Since its inception as a business unit with the WGBH Educational Foundation and subsequent evolution to a freestanding entity, CDP has driven $150 million in incremental net value back to stations and has become the most widely adopted station service in the history of public broadcasting. Just as CDP is committed to supporting public media organizations around the country, we are equally committed to supporting each of our team in their own professional development. For more information visit www.cdpcommunity.org .

• Design, develop, and deploy machine learning models for real-world production use cases • Analyze large and complex datasets to extract insights that inform model development and optimization • Build end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment • Collaborate with data engineers, software engineers, product managers, and business stakeholders to define machine learning requirements • Implement model monitoring, performance tracking, and retraining strategies • Optimize models for scalability, performance, and reliability in cloud-based environments • Ensure data quality, reproducibility, and adherence to best practices in ML development • Translate machine learning outcomes into clear, actionable insights for technical and non-technical audiences • Contribute to improving ML standards, tools, and best practices across teams • Mentor junior data scientists and machine learning engineers

• Design, develop, and deploy machine learning models for real-world production use cases • Analyze large and complex datasets to extract insights that inform model development and optimization • Build end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment • Collaborate with data engineers, software engineers, product managers, and business stakeholders to define machine learning requirements • Implement model monitoring, performance tracking, and retraining strategies • Optimize models for scalability, performance, and reliability in cloud-based environments • Ensure data quality, reproducibility, and adherence to best practices in ML development • Translate machine learning outcomes into clear, actionable insights for technical and non-technical audiences • Contribute to improving ML standards, tools, and best practices across teams • Mentor junior data scientists and machine learning engineers