Data Scientist Intern

The Data Scientist Intern supports the use of data, analytics, and machine learning to improve healthcare
outcomes and operational efficiency within a healthcare SaaS environment. Under the guidance of experienced
data scientists and cross-functional partners, the intern will assist with analyzing datasets, building models, and
generating insights that inform product, engineering, and business decisions. The role provides hands-on
exposure to real-world healthcare data and applied data science practices.

Key Responsibilities:
1. Foundational Data Science & Analytics Skills
• Assist with data exploration, cleaning, and preparation for analysis and modeling.
• Support development and evaluation of basic statistical or machine learning models under supervision.
• Apply foundational concepts in probability, statistics, and data analysis to assigned problems.
2. Data Engineering & Tooling Awareness
• Support data workflows that may include structured and unstructured datasets.
• Gain exposure to ETL concepts, data pipelines, and cloud-based data environments.
• Assist with querying datasets using SQL and/or Python-based tools.
3. AI Readiness & Model Awareness
• Develop familiarity with supervised and unsupervised learning concepts and how models are used in
production systems.
• Support model validation, basic experimentation, or feature analysis as appropriate for intern-level
work.
• Build awareness of ethical AI principles such as data privacy, bias, and responsible use.
4. Data Visualization & Communication
• Help create basic visualizations, summaries, or dashboards to communicate findings.
• Translate analytical results into clear insights for technical and non-technical stakeholders with
guidance.
• Contribute to documentation of analyses, assumptions, and learnings.
5. Healthcare Domain & Compliance Awareness
• Build foundational understanding of healthcare data concepts and workflows.
• Follow team standards related to data privacy, security, and compliance (e.g., HIPAA awareness).
• Learn how healthcare data standards (e.g., HL7, FHIR) influence analytics and modeling work
6. Collaboration & Learning
• Collaborate with data scientists, engineers, and product partners as part of an agile team.
• Participate in team meetings, standups, and reviews as a learning experience.
• Demonstrate curiosity, willingness to learn, and openness to feedback

Required Qualifications:

Education & Experience
• Currently pursuing a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics,
Mathematics, or a related field. Graduating December 2026 or later. 
• Academic, research, or project experience involving data analysis, statistics, or machine learning
(coursework, capstone, or personal projects).
• Interest in healthcare, SaaS, analytics, or applied data science.

 Other Preferred Knowledge, Skills, Abilities or Certifications:
• Programming & Analysis Tools: Python and/or R; basic SQL knowledge.
• Data & ML Libraries: Exposure to pandas, NumPy, scikit-learn, TensorFlow, or PyTorch (coursework
acceptable).
• Visualization Tools: Familiarity with Tableau, Power BI, or Python visualization libraries (e.g., matplotlib,
seaborn).
• Cloud Exposure: Awareness of AWS, Azure, or GCP data services is a plus.
• AI & Ethics Awareness: Interest in responsible AI, data privacy, and bias mitigation.
• Communication: Ability to explain analytical findings clearly and concisely.