Job Description
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Company Overview
An education-focused organization that uses technology, data, and artificial intelligence to support schools, educators, and students. The organization develops digital products and services designed to improve student engagement, retention, academic outcomes, and the overall education experience.
Responsibilities
- Develop and deploy AI-powered data products using applied machine learning techniques across large datasets.
- Design, evaluate, and productionize machine learning models following standardized software engineering and coding practices.
- Build APIs, AI models, data pipelines, and supporting resources that transform data into actionable business insights.
- Partner with Product, Engineering, IT, data engineering, and data stewardship teams to identify trends, solve complex problems, and develop practical AI solutions.
- Ensure AI workflows use high-quality data and follow appropriate security, privacy, scalability, and reliability practices.
- Translate business requirements into innovative and practical AI solutions that produce measurable outcomes.
- Establish processes to ensure AI outputs are measurable, documented, reproducible, and continuously monitored for performance and model drift.
- Support continuous model improvement and retraining when necessary.
- Gather requirements, organize data sources, and integrate AI models into products throughout the product lifecycle.
- Provide technical guidance and thought leadership on initiatives focused on improving academic and organizational outcomes through applied AI.
- Support the adoption of AI capabilities and actionable data across education and school-service teams.
- Identify opportunities to apply AI across business units and functions in alignment with organizational objectives.
- Collaborate with cross-functional teams to prioritize initiatives and facilitate work on complex AI and machine learning projects.
Qualifications
- Bachelor’s degree in Computer Science, Mathematics, Physics, Engineering, or a related quantitative field with 6+ years of relevant experience, or an equivalent combination of education and experience.
- Hands-on experience with modern machine learning libraries and Python-based frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Strong understanding of statistical principles and applied machine learning techniques.
- Experience with AWS, Azure, Docker, Kubernetes, and Terraform for building scalable, secure, and high-performing environments.
- Experience designing, implementing, and maintaining robust machine learning pipelines.
- Knowledge of version control, containerization, and CI/CD processes.
- Ability to validate model outputs and maintain model integrity, reliability, accuracy, and usefulness.
- Strong analytical and problem-solving skills with excellent attention to detail.
- Ability to manage multiple projects and priorities while meeting critical deadlines.
- Strong verbal and written communication skills.
- Ability to work independently and collaboratively within cross-functional teams.
- Strong customer-service orientation and professional integrity when handling confidential information.
- Proficiency with Microsoft Office applications, including Word, Excel, PowerPoint, and Outlook.
- Experience with modern reporting platforms and tools.
- Ability to understand complex technology platforms, business models, and digital environments.
- Ability to successfully complete required background checks.
- A Master’s or Doctorate degree is preferred.
- Additional experience with MLOps frameworks such as MLflow or Kubeflow is a plus.
- Experience with advanced machine learning techniques, including natural language processing, recommender systems, or deep learning, is a plus.
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