Data Scientist I

HelioCampus•Bethesda, WI

3d•Remote

About The Position

HelioCampus is a growing EdTech company focused on helping college and university leaders navigate challenges through data and Institutional Performance Management practices. They aim to chart a path to sustainability for their clients by applying business best practices. The company culture is characterized by a "we're on it" attitude, a commitment to simplifying complex problems with data, and transparency. They are looking for change agents who embrace challenges. This role is within the Client Experience division, specifically on the DS-OPS (Data Science Operations, Products, and Services) team. The Data Scientist will partner with university leadership to translate business questions into data questions, delivering analyses and predictive models to support institutional decision-making. Responsibilities include designing datasets, performing statistical analysis, building dashboards, and deploying machine learning models. The role requires independent project management, strong communication skills to translate complex analytical results into actionable insights for higher education stakeholders, and collaboration with engineers and clients.

Requirements

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 systems
Ability 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 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 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.