ML/Data Scientist

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 ML Engineer/Data Scientist will be responsible for designing, developing, and deploying machine learning models and data-driven products focused on student audiences and AI-enabled career advising. This role will involve building and maintaining algorithms, data pipelines, and analytical solutions to enhance student engagement and career outcomes. The ML Engineer/Data Scientist will ensure that our data products are robust, scalable, and effectively address the evolving needs of students and educational institutions.

Duties and Responsibilities:

• Machine Learning and Algorithm Development

  • Design, develop, and implement machine learning models and algorithms for student audience analysis and AI-enabled career advising.
  • Utilize various machine learning techniques, including predictive modeling, recommendation systems, and natural language processing.
  • Evaluate and optimize model performance, ensuring accuracy, fairness, and interpretability.
  • Develop and maintain data pipelines to support ML model training, evaluation, and deployment.

• Data Product Development

  • Collaborate with product managers and stakeholders to define requirements for data products and features.
  • Build and maintain data-centric applications and tools that leverage machine learning insights.
  • Implement and manage CI/CD workflows for ML models and data product deployments.
  • Ensure data quality and integrity across all data products and analytical solutions.

• Collaboration and Communication

  • Serve as a primary conduit among business leads, product managers, and data/engineering teams.
  • Facilitate demos, knowledge-sharing sessions, and best-practice documentation.
  • Promote adoption of new capabilities and practices; gather feedback for continuous improvement.

• Release and Change Management

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

Knowledge/Skills/Abilities:

• Ability to produce and manage, work autonomously, and take initiative.
• “Can-do” attitude with unwavering ethics and a willingness to help others.
• Exceptional relationship-building skills, cultural competency, and ability to communicate effectively with diverse groups of people and 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.
• Strong business analysis fundamentals: elicitation, documentation, process mapping, traceability, UAT.

Requirements:

• 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 data querying and manipulation.
• Experience with Git and Git providers (e.g., GitHub, BitBucket, GitLab).
• Understanding of statistical analysis and experimental design.

Nice-to-haves:

• Experience with natural language processing (NLP) techniques and tools.
• Familiarity with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn).
• Experience with A/B testing and experimentation frameworks.
• Knowledge of educational technology or career development domains.

Credentials and Experience:

• College degree or equivalent life/work experience in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering).
• 5+ years in machine learning engineering, data science, or a related analytical role within SaaS, marketing technology, higher ed tech, or related domains.

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/Sport Budget.
• Fully remote.