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Applied Machine Learning Intern Jobs in Colorado

The ideal candidate brings deep experience in applied machine learning, strong technical judgment, and the ability to collaborate across engineering and analyst teams while mentoring other engineers ...

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Applied Machine Learning Intern information

What is the difference between Applied Machine Learning Intern vs Data Science Intern?

AspectApplied Machine Learning InternData Science Intern
Required SkillsMachine learning algorithms, programming (Python, R), data analysisStatistical analysis, data visualization, programming (Python, R)
Work EnvironmentDeveloping ML models, experimenting with algorithms, deploying modelsData cleaning, analysis, reporting insights
Industry UsageTech companies, AI startups, research labsBusiness analytics, market research, finance

Applied Machine Learning Interns focus on developing and deploying machine learning models, requiring knowledge of algorithms and programming. Data Science Interns typically handle data analysis, visualization, and reporting. While both roles involve data skills, applied ML interns work more on model implementation, whereas data science interns focus on insights and data interpretation.

What are popular job titles related to Applied Machine Learning Intern jobs in Colorado?

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What cities in Colorado are hiring for Applied Machine Learning Intern jobs?

Cities in Colorado with the most Applied Machine Learning Intern job openings:

Senior Data Scientist - Risk & Compliance Domain

Denver, CO โ€ข On-site

Full-time

Re-posted 2 days ago


Job description

Overview:
Job Title: Senior Data Scientist - Risk & Compliance Domain (Job ID: 2098)
Location: Remote (but need someone local to - Denver, CO 80237)
Duration: July 15, 2025 - February 27, 2026
Hire Type: Contractor (Contract Only)
Role Responsibilities:
Project Management: Leading and managing end to end data science projects, defining objectives, define requirements, and ensuring timely delivery.
Model Development & Implementation: Developing and validating machine learning and statistical models, implementing predictive models, and optimizing existing ones.
Data Analysis & Insights: Analyzing large datasets, identifying trends and patterns, and extracting actionable insights to inform business decisions.
Collaboration & Communication: Working with cross-functional teams and stakeholders, including engineering, product, and business stakeholders, to translate complex data into actionable strategies.
Mentorship & Guidance: Providing guidance and mentorship to junior data scientists, sharing expertise, and fostering their professional growth.
Staying Updated: Keeping abreast of the latest advancements in data science and AI, exploring new tools and techniques, and identifying opportunities for innovation.
Strategic Input: Contributing to the development of data-driven strategies and providing input on technical approaches for projects.
Role Requirements:
Bachelor's degree in Computer Science, Statistics, Applied Math, or related field (Master's or PhD strongly preferred).
5+ years of industry experience in applied machine learning, with 2+ years focused on deep learning and neural network applications.
Experience in Banking, Payments or Financial Services leading in detecting and preventing digital fraud, scams and social engineering.
Able to quickly understand our current Risk Models with the goal of building a flexible AI solution that partners with our current risk tools to maintains our high standards for Risk Prevention and Compliance while providing flexibility to effectively run our business.
Expertise in statistical modeling, machine learning algorithms, and data mining techniques.
Experience navigating difficult scenarios developing and implementing execution ideas with minimal guidance.
Proficiency in programming languages like Python or R.
Ability to create clear and concise visualizations to communicate complex data.
Ability to create clear and standard data models to communicate with the stakeholders and to capture the semantics of the data and the complex semi-structured content.
Strong analytical and problem-solving skills, with the ability to tackle complex business challenges.
Excellent communication and presentation skills to effectively convey findings to both technical and non-technical audiences.
Experience and willingness to lead and mentor a team, providing guidance and support to junior members
Skills:
Risk & Compliance