About the Opportunity:
Carnegie has been a leader and innovator in higher education marketing and enrollment strategy for over 30 years, offering groundbreaking services in research, strategy, digital marketing, lead generation, slate optimization, student search, website development, and creative that generate authentic connections.
Job Overview
The Director of Data Insights will define the strategic vision, lead the development, and oversee the deployment of advanced machine learning models and data-driven products. This senior role focuses on student audience segmentation, predictive propensity modeling, and AI-enabled career advising. The Director of Data Insights will manage a team of data scientists and engineers, directing the creation and maintenance of robust, scalable data pipelines and analytical solutions to enhance student engagement, career outcomes, and institutional effectiveness.
* Data Science Strategy and Team Leadership
* Lead, mentor, and manage a team of ML/Data Scientists, fostering a culture of technical excellence and continuous improvement.
* Define the technical roadmap and best practices for all data science initiatives, focusing on model reliability, fairness, and interpretability.
* Direct the design, development, and implementation of high-impact data products, especially those focused on segmentation and propensity models.
Key Responsibilities:
* Data Science Strategy and Team Leadership
* Advanced Model and Algorithm Development
* Data Product Development
* Collaboration and Communication
* Release and Change Management
Requirements:
* Minimum of 10 years of experience in machine learning engineering, data science, or a related analytical/leadership role within SaaS, marketing technology, higher ed tech, or related domains.
* Demonstrated experience in managing and mentoring a team of data scientists or ML engineers.
* Expert-level experience building and deploying segmentation and propensity models in a commercial setting.
* Familiarity with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).