Data Scientist I

Who We Are: HelioCampus was founded with the purpose of helping college & university leaders navigate today’s growing pressures. As one of EdTech’s most steadily growing companies, our roots in higher ed ground us in the critical nature of this work.What We Do:We help our clients navigate today’s high stakes with data and Institutional Performance Management practices. Together with our clients, we chart a path to sustainability by borrowing practices from the most successful businesses.How We Do It: We approach our work with a “we’re on it” attitude that the higher ed community has come to know and expect from our team. We’re data geeks to our core, committed to simplifying the toughest challenges and operating with transparency. Our most successful team members are change agents who love a challenge.How You'll Help:Bringing experience analyzing student-level data at a Higher Ed institution or EdTech company in the areas of admissions, enrollment, financial aid, student success, institutional effectiveness, or finance and budget, you will step into a key role within HelioCampus’ Client Experience division. As a member of the DS-OPS (Data Science Operations, Products, and Services) team, you will partner directly with university leadership to turn "business questions" into "data questions," delivering analyses and predictive models that support institutional decision-making.In this role, you will work independently and collaboratively with engineers and clients to design datasets, perform statistical analysis, build dashboards, and deploy machine learning models. Managing your projects autonomously, you will use your strong communication skills to translate complex analytical results into clear, actionable insights for a variety of higher education stakeholders.What You Will Do: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.You Will Be a Good Fit For This Role If You Have:Experience analyzing student-level data at a Higher Ed institution or EdTech company 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 experience required).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.Experience working with student data from PeopleSoft, Banner, Colleague, Workday, Slate, Salesforce/TargetX, or other higher ed-specific data systemsAbility to work effectively and independently in a remote role on an Eastern time zone business hour schedule , 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.Nice To Have’s Include:Advanced degree in an analytical/quantitative field (Comparable depth of experience can be substituted for quantitative field of study or advanced degree).Experience working remotely with distributed teams and clients.Experience with version control (gi