Head of Data Science

About the Product:

For more than 30 years, Carnegie has been a leader and innovator in higher education marketing and enrollment strategy, offering groundbreaking services in the areas of Research, Strategy, Digital Marketing, Lead Generation, Slate Optimization, Student Search, Website Development, and Creative that generate authentic connections.

Job Purpose:

The Head of Data Science will be responsible for defining the strategic vision, leading the development, and overseeing the deployment of advanced machine learning models and data-driven products. This senior role will specifically focus on student audience segmentation, predictive propensity modeling, and AI-enabled career advising. The Head of Data Science will manage a team of data scientists and engineers, directing the creation and maintenance of robust, scalable data pipelines and analytical solutions to significantly enhance student engagement, career outcomes, and overall institutional effectiveness.

Duties and Responsibilities:

• 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.

• Advanced Model and Algorithm Development

  • Design, develop, and implement machine learning models and algorithms for complex student audience analysis and AI-enabled career advising, with a strong focus on predicting student behavior and optimizing engagement strategies.
  • Oversee the utilization of advanced machine learning techniques, including predictive modeling, recommendation systems, and natural language processing.
  • Establish rigorous processes for model evaluation, optimization, and monitoring in production environments.
  • Develop and maintain robust, scalable data pipelines to support all phases of the ML lifecycle.

• Data Product Development

  • Collaborate closely with product managers and business stakeholders to translate strategic requirements into data product features.
  • Direct the building and maintenance of data-centric applications and tools that leverage machine learning insights.
  • Implement and manage MLOps and CI/CD workflows for efficient model and data product deployments.
  • Ensure data governance, quality, and integrity across all analytical solutions.

• Collaboration and Communication

  • Serve as the primary technical conduit among executive business leads, product management, and data/engineering teams.
  • Facilitate demos, strategic reviews, knowledge-sharing sessions, and best-practice documentation.
  • Drive the adoption of new data science capabilities and gather feedback for continuous strategic alignment.

• Release and Change Management

  • Coordinate comprehensive release plans, timelines, and stakeholder readiness for ML model and data product deployments.
  • Ensure training, job aids, and rollout communications are prepared and delivered effectively.
  • Track and report on post-release issues, adoption metrics, and stabilization progress.

Knowledge/Skills/Abilities:

• Proven ability to set technical direction, produce high-quality work, manage autonomously, and take strategic initiative.
• Strong business acumen with unwavering ethics and a willingness to lead by example.
• Exceptional relationship-building skills, cultural competency, and ability to communicate effectively with diverse groups of people and executive roles.
• Willingness to embrace relational nuances, own personal mistakes, be empathetic, address conflicts directly and transparently, and commit to self-reflection and self-betterment.
• Dexterity to effectively deal with ambiguity, change, and continuous process improvements at a strategic level.
• Strong business analysis fundamentals: elicitation, documentation, process mapping, traceability, UAT.
• Advanced English proficiency (B2–C1 or higher)

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).
• Experience with data manipulation and analysis using Python (e.g., Pandas, NumPy).
• Experience with cloud-based data platforms (e.g., BigQuery, Redshift, GCS, S3).
• Proficiency in SQL for complex data querying and manipulation.
• Experience with Git and Git providers (e.g., GitHub, BitBucket, GitLab).
• Deep understanding of statistical analysis, experimental design, and A/B testing methodologies.

Nice-to-haves:

• Experience with media modeling audiences and digital lookalike techniques.
• Experience with natural language processing (NLP) techniques and tools.
• Familiarity with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn).
• Knowledge of educational technology or career development domains.

Credentials and Experience:

• PhD in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.

We offer:

• We welcome new ideas and allow you to make an immediate impact on the team.
• Flexible Paid time off (PTO for any reason, including sick days (no specified limits) and flexible work schedule.
• Personal laptop.
• Health and wellness package.
• Fully remote.