Über
Communicate with stakeholders to determine institutional goals, and design analyses and data visualizations to provide insight to complex business problems. Conduct exploratory data analysis in collaboration with subject matter experts to build and validate analytical datasets. Develop statistical and machine learning models (classification, time series, etc.) using a custom python framework to generate scores and forecasts. Present predictive modeling results and operational dashboards to end-users at colleges and universities to help them understand and make use of the findings and scores. Work with data science and data engineering teams to build data pipelines, improve internal systems, and deploy and maintain models in a production environment. Collaborate with other data scientists to share knowledge, develop best practices, and contribute to documentation, process standardization, and product development. Occasionally travel (typically 1-3 days at a time, ~3 times per year) to HelioCampus offices and client sites for meetings and presentations. Requirements:
Experience building analyses using educational industry data at a Higher Ed institution or EdTech company, especially in the areas of admissions, enrollment, financial aid, student success, institutional effectiveness, or finance/budget. 3+ years of experience delivering analytical insights to higher ed stakeholders, including building and evaluating machine learning models (python and scikit-learn experience required). Analytical dataset design and feature engineering skills (SQL and python). Ability to conduct, interpret, and explain statistical analyses. Experience using interactive data visualization and business intelligence tools (Tableau and/or Power BI) to design and publish interactive reports and dashboards, enabling data exploration and communication of analysis results. Excellent communication and collaboration skills, and experience working with both business users and technical development teams, as well as presenting findings to decision-makers. Ability to work effectively and independently in a remote role, managing multiple priorities and meeting deliverable deadlines. Understanding of model transparency & explainability concepts, and ethical issues in data science. Familiarity with production data pipeline and model deployment and management. Familiarity with relational database and data warehouse concepts. Familiarity with a variety of machine learning methodologies, forecasting techniques, and generative AI (LLMs). A tool-agnostic approach to data science: excitement for adopting new tools and techniques, while having solid fundamentals that underpin quick learning and high quality work delivery. Benefits:
Competitive salary Paid time off Health insurance Vision Dental 401(k) w/ company match Parental leave Remote work flexibility Home office perks Collaborative work environment
Sprachkenntnisse
- English
Hinweis für Nutzer
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