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Apprentice Machine Learning Testing Jobs in Maine

Northeastern University

Data Scientist

Portland, ME · On-site

... or machine learning. • Solid understanding of statistical methods, regression, hypothesis testing, and basic experimental design. • Hands-on experience with classical machine learning methods ...

Northeastern University

Data Scientist

Portland, ME · On-site

... 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 ...

NorthEastern

Data Scientist

Portland, ME

$87K - $123K/yr

Solid understanding of statistical methods, regression, hypothesis testing, and basic experimental design. * Hands-on experience with classical machine learning methods such as linear/logistic ...

Northeastern University

Data Scientist

Portland, ME · On-site

$87K - $123K/yr

Solid understanding of statistical methods, regression, hypothesis testing, and basic experimental design. * Hands-on experience with classical machine learning methods such as linear/logistic ...

ALIGN PRECISION

Senior CNC Machinist

Arundel, ME · On-site

$35 - $55/hr

May operate more than one machine simultaneously. * Completes all paperwork accurately and keeps ... We thrive through continual learning and mentoring. From apprenticeship to leadership, our people ...

Loyalty360

Manager Advanced Analytics

Scarborough, ME · On-site

$108.88 - $187.80/hr

... machine learning, operations research, econometrics, and business analysis. Work closely with ... Design tests for quantifying supply chain pilot results using A/B testing and statistical methods

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Apprentice Machine Learning Testing information

What kinds of projects or tasks can I expect to work on as an apprentice machine learning testing?

As an Apprentice Machine Learning Testing, you’ll typically assist in evaluating machine learning models by designing and running tests, analyzing model outputs, and helping identify issues like bias or overfitting. You may work closely with data scientists and software engineers to validate model performance and ensure results align with project objectives. Your daily tasks might include preparing test datasets, executing automated testing scripts, and documenting findings to help improve model reliability. This role often serves as a valuable introduction to practical machine learning workflows and quality assurance processes in technical teams.

What are the key skills and qualifications needed to thrive as an apprentice machine learning testing, and why are they important?

To thrive as an Apprentice in Machine Learning Testing, a foundational understanding of statistics, programming (especially Python), and basic machine learning concepts is essential, often supported by a degree or coursework in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, Jupyter Notebooks, and version control systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help apprentices collaborate and identify testing issues efficiently. These skills ensure accurate model validation, effective troubleshooting, and contribute to the robust deployment of machine learning solutions.

What does an apprentice machine learning testing do?

An Apprentice Machine Learning Testing professional assists in evaluating and validating machine learning models to ensure they perform as expected. They typically work under the guidance of experienced data scientists or engineers, running tests, analyzing results, and helping to identify issues such as bias or inaccuracies in algorithms. Their responsibilities may also include developing test cases, writing reports, and learning about data preprocessing and evaluation metrics. This role is ideal for those who are new to the field and want to build foundational skills in machine learning quality assurance.

What is the difference between Apprentice Machine Learning Testing vs Machine Learning Engineer?

AspectApprentice Machine Learning TestingMachine Learning Engineer
Required CredentialsBasic understanding of ML concepts, often pursuing relevant certifications or degreesAdvanced degrees (BSc, MSc, PhD) in CS or related fields, with extensive experience
Work EnvironmentEntry-level, supervised testing environments, often in training programsFull-time, independent development and deployment of ML models in production
Employer & Industry UsageInternships, training programs, entry-level roles in tech companiesEstablished tech firms, startups, research institutions

Apprentice Machine Learning Testing roles focus on learning and assisting with testing ML models under supervision, while Machine Learning Engineers design, build, and deploy ML systems independently. The apprentice position is ideal for gaining foundational skills, whereas the engineer role requires advanced expertise and experience.

What are popular job titles related to Apprentice Machine Learning Testing jobs in Maine?

For Apprentice Machine Learning Testing jobs in Maine, the most frequently searched job titles are:

What job categories do people searching Apprentice Machine Learning Testing jobs in Maine look for?

The top searched job categories for Apprentice Machine Learning Testing jobs in Maine are:

What cities in Maine are hiring for Apprentice Machine Learning Testing jobs?

Cities in Maine with the most Apprentice Machine Learning Testing job openings:

Apprentice Machine Learning Testing jobs near you
Infographic showing various Apprentice Machine Learning Testing job openings in Maine as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 19% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Data Scientist

Northeastern University

Portland, ME • On-site

Full-time

Re-posted 21 days ago

Job description

Job Summary:
Northeastern University is seeking a Data Scientist for its AI Solutions Hub at the Roux Institute in Portland, Maine. This role is designed for early-career data scientists to contribute to the development and delivery of AI and data science solutions across various industries while gaining exposure to production systems and modern AI practices.
Responsibilities:
• 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.
Qualifications:
Required:
• Master’s degree 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.
• 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.
• 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).
• 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.
• 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.
Preferred:
• 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.
Company:
Founded in 1898, Northeastern is a global research university with a distinctive, experience-driven approach to education and discovery. Founded in 1898, the company is headquartered in Boston, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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