Data Scientist Intern

Fortive•Minneapolis, MN

About The Position

Job Summary: 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 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. 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. 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. 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. 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 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

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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
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