Data Scientist Intern

Portage Learning•Chippewa Township, PA

39d•Onsite

About The Position

The Data Science Intern will work closely with members of the Data Science team and collaborate with Engineering and other departments. The Data Scientist Intern will contribute to data analysis and data science projects throughout the project lifecycle. This may include preparing and exploring datasets, conducting statistical analyses, developing and evaluating machine learning models, creating visualizations, and communicating findings to technical and non-technical stakeholders. Portage Learning's mission is to transform lives and communities by providing learning opportunities for all who aspire to thrive, through superior online instruction and a deep commitment to expanding access to education. The company values believing the best in employees, investing in people, collaborating with humility, respect, and appreciation, and cultivating curiosity and encouraging creativity.

Requirements

Currently pursuing an undergraduate degree in data science, computer science, business analysis, statistics, or related field.
Coursework or project experience involving Python, SQL, statistics, data analysis, or machine learning.
Experience working with datasets through coursework, personal projects, research, internships, or other relevant activities.
Successful completion of a background check and current and/or ability to obtain the following clearances: Child Abuse, State Police, and FBI Fingerprinting.
Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Nice To Haves

Working knowledge of Python for data analysis and data science.
Familiarity with SQL and relational databases such as MySQL.
Familiarity with common Python data science libraries such as pandas, NumPy, scikit-learn, Matplotlib, or similar tools.
Understanding of fundamental statistical concepts, including descriptive statistics, probability, hypothesis testing, correlation, and regression.
Basic understanding of machine learning concepts and common supervised and unsupervised learning techniques.
Ability to clean, transform, explore, and analyze structured datasets.
Ability to create meaningful data visualizations and communicate analytical results.
Familiarity with version control tools such as Git is preferred.
Exposure to cloud computing, generative AI, large language models, or modern data science tools is beneficial but not required.
Strong analytical thinking and problem-solving skills.
Curiosity and willingness to investigate data and ask meaningful questions.
Ability to interpret analytical results and explain findings clearly.
Attention to detail when working with data, code, and analytical results.
Ability to manage multiple tasks, prioritize work, and meet deadlines.
Ability to work independently while seeking guidance when appropriate.
Ability to collaborate effectively within a multidisciplinary team.
Strong written, verbal, listening, and presentation skills.
Service-oriented mindset with a willingness to help others.
Demonstrated interest in applying data science and analytical methods to real-world problems.
Willingness to learn new technologies, analytical techniques, and business domains.

Responsibilities

Assist Data Scientists with collecting, cleaning, transforming, and validating data for analysis and modeling.
Perform exploratory data analysis (EDA) to identify patterns, trends, relationships, anomalies, and potential data quality issues.
Apply statistical methods to investigate business questions and support data-driven decision-making.
Assist with developing, testing, and evaluating machine learning models using techniques such as regression, classification, and clustering.
Evaluate model performance using appropriate metrics and assist with comparing modeling approaches.
Write and maintain Python and SQL code used for data extraction, transformation, analysis, and modeling.
Create clear visualizations, dashboards, reports, and summaries that communicate analytical findings to technical and non-technical audiences.
Assist with monitoring and evaluating existing analytical models and data science solutions.
Document analytical methods, assumptions, data definitions, model results, and project findings to support reproducibility and knowledge sharing.
Collaborate with Data Science, Engineering, and other departments to understand business questions and translate them into analytical tasks.
Participate in code reviews, project discussions, technical learning activities, and other team development opportunities.
Research data science, machine learning, artificial intelligence, and analytical methods that may be relevant to company projects.
Present project findings and recommendations to team members and other stakeholders when appropriate.
Engage in weekly professional development sessions as part of the summer internship cohort, cultivating collaboration, strengthening skills, and encouraging continuous learning.
Perform other related duties as assigned.