Internship Python Gis Developer Jobs in Portland, ME

About the Opportunity

JOB SUMMARY

This is a full-time, one-year term appointment with the possibility of renewal. The position is in-person at Northeastern's Roux Institute in Portland, Maine.

The Data Scientist at the AI Solutions Hub (AISH), the delivery arm of Northeastern University's Experiential AI Institute, will support the development and delivery of AI and data science solutions across diverse industries. The role is designed for early-career data scientists who will work under the guidance of senior data scientists, AI engineers, and faculty leads.

The Data Scientist will contribute to data analysis, feature engineering, model development, evaluation, and documentation, while progressively gaining exposure to production systems, client-facing work, and modern AI practices across Predictive AI and Generative AI use cases.

• Master's degree (required) or Ph.D. (optional) in Computer Science, Engineering, Applied Mathematics, Statistics, or a closely related field.

• 0-2 years of industry, research, or applied project experience in data science or machine learning.

• Experience gained through internships, co-ops, academic research, or applied capstone projects is acceptable.

• Industry experience is preferred.

• Solid understanding of statistical methods, regression, hypothesis testing, and basic experimental design.

• Hands-on experience with classical machine learning methods such as linear/logistic regression, decision trees, and gradient boosting.

• Familiarity with deep learning concepts and modern architectures (e.g., convolutional neural networks or transformers); deep specialization is not required.

• Exposure to Generative AI concepts and large language models (LLMs) is a plus.

• Proficiency in Python for data analysis and model development (NumPy, pandas, scikit-learn).

• Working knowledge of SQL and relational databases.

• Familiarity with at least one ML or deep learning framework (e.g., PyTorch, TensorFlow, HuggingFace).

• Perform data cleaning, exploratory data analysis (EDA), and feature engineering.

• Train, evaluate, and compare machine learning models under supervision.

• Assist with model validation, performance monitoring, and documentation.

• Contribute to ML pipelines and collaborate with ML engineers on deployment-related tasks.

• Ability to clearly communicate analytical findings to technical and non-technical audiences with guidance.

• Collaborate effectively with cross-functional teams including data scientists, engineers, project managers, and faculty experts.

• Willingness to participate in client meetings in a supporting role.

• Exposure to NLP, computer vision, or speech processing through coursework or academic/industry projects.

• Familiarity with cloud platforms (AWS, Azure, or GCP).

• Understanding of software development best practices such as version control (Git) and Agile workflows.

• Awareness of ethical AI principles including fairness, transparency, and responsible model use.

• Willingness to follow established governance, documentation, and review practices.

• Strong curiosity and motivation to learn new tools, techniques, and AI methods.

• Openness to feedback and mentorship.

• Ability to manage assigned tasks, meet deadlines, and maintain high-quality work.

• Proactive attitude and willingness to take increasing responsibility over time.

Position Type

Additional Information

Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.

All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law.

Compensation Grade/Pay Type:

Expected Hiring Range:

With the pay range(s) shown above, the starting salary will depend on several factors, which may include your education, experience, location, knowledge and expertise, and skills as well as a pay comparison to similarly-situated employees already in the role. Salary ranges are reviewed regularly and are subject to change.