Data Scientist I

HelioCampus•Bethesda, WI

1d•Remote

About The Position

As a Data Scientist, you will partner with college and university stakeholders to turn business questions into data questions and deliver analyses, predictive models, reports, and AI/ML solutions that support institutional decision-making and student success. You will design and prepare analytical datasets, conduct exploratory and statistical analysis, develop and maintain machine learning and forecasting models, configure and deploy AI/ML products, build interactive dashboards, and clearly communicate insights to higher education leaders and staff. In this role, you’ll work independently and collaboratively with data scientists, analysts, engineers, and client stakeholders to deliver meaningful, actionable results across student success, institutional effectiveness, and operational efficiency.

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.

Nice To Haves

Advanced degree in an analytical/quantitative field (Comparable depth of experience can be substituted for quantitative field of study or advanced degree).
Experience working with student data in PeopleSoft, Banner, Colleague, Workday, Slate, Salesforce/TargetX, or other higher ed-specific data systems.
Experience working remotely with distributed teams and clients.
Experience with version control (git), software development processes, object oriented programming, and machine learning framework development.
MLOps experience deploying and maintaining production pipelines and models.
Experience applying a wide range of supervised and unsupervised machine learning and forecasting techniques using a variety of python packages.
Experience working with any of the following tools: jupyter notebooks, pandas, Docker, linux server/command line, EC2, S3, Amazon Redshift, airflow, SHAP, MLFlow, prophet, VS Code.
Experience using AI tools/services to increase delivery efficiency without sacrificing quality (for example - having built enough depth of coding experience to critically evaluate AI-generated code and avoid introducing issues that lead to future rework).
Experience with LLM Evaluation methods and frameworks.

Responsibilities

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.