Lead Data Scientist – Student Success Analytics

Role Overview

We are seeking an experienced Lead Data Scientist to spearhead the machine learning development for InsightStream AI, our innovative student success platform. Your primary role will involve creating predictive models to identify at-risk students and generate intelligent intervention recommendations. This position focuses on utilizing the Snowflake environment to specifically support smaller private institutions and community colleges. As a key player in our fast-growing team, you will be instrumental in achieving a critical 3-4 month timeline to launch our case management prototype.

Responsibilities

• Lead the development of machine learning models aimed at predicting student risk and recommending interventions.
• Construct classification models using Snowpark ML to identify at-risk students and forecast enrollment outcomes.
• Develop transparent and explainable algorithms that can be easily understood and trusted by academic advisors.
• Create semantic models to facilitate natural language queries over student success datasets.
• Mentor the data science team and establish machine learning best practices as the team expands.
• Collaborate with the development team to integrate ML models with the case management system.
• Work with data from institutional partners to refine models for diverse student demographics.
• Rapidly prototype and iterate to align with the aggressive product development timeline.

Required Skills

• Expertise in Snowflake and Snowpark, with a preference for experience in Snowpark ML model development and deployment.
• Proficiency in classification modeling, particularly for student risk prediction, enrollment probability, and attrition forecasting.
• Strong skills in Python and SQL for building scalable ML pipelines within the constraints of Snowflake.
• Ability to create explainable models that offer transparent ML recommendations.
• Experience with semantic data modeling to enable natural language queries over educational datasets.
• Skills in anomaly detection to identify unusual student behavior patterns.

Nice to Have

• 3+ years of experience in higher education data, including student information systems, retention analytics, and student success metrics.
• In-depth knowledge of the higher education domain, including student lifecycle, intervention strategies, and institutional challenges.
• Team leadership experience, including mentoring, project oversight, and cross-functional collaboration.
• Awareness of FERPA and data security, ensuring compliance with educational data privacy requirements.
• Ability to translate complex ML concepts for non-technical stakeholders.
• Strong project management skills, particularly in a fast-paced startup environment.
• Experience working with the resource constraints of smaller educational institutions.
• Excellent communication skills for working with institutional partners and internal teams.