Job Overview
The Head of Data Science is a senior leadership role that oversees the development and deployment of advanced machine learning models and data-driven products.
Duties and Responsibilities:
* 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.
* 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.
* 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.
* 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.
* 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.
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.
Benefits:
* 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, and flexible work schedule.
* Personal laptop.
* Health/Sport Budget.
* Fully remote.