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.
Who You Are:
You are a data scientist who has worked with college & university stakeholders to convert “business questions” into “data questions” and apply analytical and predictive modeling techniques to deliver actionable insights. You demonstrate strong written and verbal communication skills, can manage your projects independently, and are excited to collaborate with other data scientists, analysts, engineers, business stakeholders, and industry clients. You have skills relevant to all stages of machine learning pipelines (from data source to deployment) as well as experience delivering and explaining analysis results in the domain of higher education.
How You'll Help:
Working within HelioCampus’ Client Success division, you will partner with college and university leadership and staff to understand their analytical needs and to design, develop, deploy, and maintain models and reports that provide insight into student success, institutional effectiveness, and operational efficiency.
As a member of the DS-OPS (Data Science Operations, Products, and Services) team, you will work both independently and in collaboration with team members and stakeholders to design and prepare analytical datasets, perform exploratory and statistical analysis, design and develop a variety of predictive models using machine learning and forecasting techniques, build interactive dashboards, and deploy and maintain production models, delivering and explaining analytical results to a variety of 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 machine learning models (classification, regression, etc.) using python to generate scores and forecasts
- Present predictive modeling results to end-users at 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, up to 4 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 working with data at a Higher Ed institution or EdTech company, especially in the areas of admissions, enrollment, financial aid, student success, and institutional effectiveness
- 3+ years of experience interacting with educational industry data systems and delivering analyses to higher ed stakeholders, with significant experience building and evaluating machine learning models (python and scikit-learn experience required)
- Analytical dataset design and preparation skills (SQL and python)
- Ability to conduct, interpret, and explain statistical analyses
- Experience using interactive data visualization and business intelligence tools (Tableau preferred) to enable data exploration and communicate results of data analysis
- 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 version control, software development processes, and object oriented programming
- 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
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 with student data in PeopleSoft, Banner, Colleague, Workday, Slate, Salesforce/TargetX, or other higher ed-specific data systems
- MLOps experience deploying and maintaining production pipelines and models
- Software engineering and object-oriented programming experience, or machine learning framework development experience
- Experience applying a wide range of supervised and unsupervised machine learning and forecasting techniques using a variety of python packages
- Experience using Tableau to design and publish interactive reports and dashboards
- Experience working with any of the following tools: jupyter notebooks, pandas, git, Docker, linux command line, EC2, S3, Amazon Redshift or PostgreSQL, airflow, SHAP, MLFlow, prophet, Tableau, Power BI
- Experience working remotely with distributed teams and clients
Example Projects You Will Work On:
- Developing classification models to predict admitted student yield, enrolled student retention, or on-time graduation
- Helping institutions understand financial aid correlation with student success metrics
- Forecasting enrollments using time-series methods
- Designing and publishing interactive dashboards to enable end-users to operationalize model results at colleges and universities
- Improving internal products and processes using machine learning
- Contributing to extensible machine learning development and deployment framework
- Designing and documenting ML workflows, data pipelines, and deployed model management and monitoring systems
Location:
This specific role is primarily remote, with occasional travel to an office or client site. HelioCampus has two locations, Bethesda, MD and Chapel Hill, NC.
Compensation and Benefits:
HelioCampus offers employees a competitive salary along with paid time off, healthcare, vision, dental, 401(k) w/ company match, parental leave, remote work flexibility, home office perks, and a fun, collaborative work environment.
What We Believe:
HelioCampus is an equal opportunity employer, and all qualified candidates will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.
Disclaimer:
The list of responsibilities described above is not comprehensive. Other tasks not mentioned here may be required. Changing business conditions may trigger changes to job responsibilities. This job description is not an employment contract.
Physical Qualifications:
This position requires the following physical qualifications to support a productive remote work environment. We are dedicated to providing reasonable accommodations for individuals with disabilities to perform the essential functions of this role. If you require accommodations, please discuss this during the application process.
Vision: Sufficient visual acuity to read documents, view computer screens, and engage in tasks that require attention to detail.
Hearing: Ability to communicate effectively in person (as applicable) and via phone or video conferencing, ensuring clear understanding of verbal instructions.
Fine Motor Skills: Proficient use of hands for typing, using a mouse, and managing office equipment such as printers and scanners.
Lifting: Occasional lifting of up to 10 pounds (e.g., office supplies or equipment).
