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Internship Applied Scientist Machine Learning Jobs in Michigan

Eccalon LLC

Machine Learning Engineer

Detroit, MI · On-site

Machine Learning Engineer Location: Detroit, MI- Onsite Type: Full-time Security Clearance: No ... Required Qualifications * BS. in Computer Science, or related field. * 3+ years of professional ...

Agile Tech Labs

Data Scientist

Detroit, MI · On-site

Data Scientist Job Location: Detroit, MI (Hybrid) Job Type: Contract ... Develop and deploy Machine Learning and AI models. * Analyze large datasets and generate business ...

FastTek

Machine Learning Engineer

Dearborn, MI · On-site

Machine Learning Engineer #1058742 Position Description: We are seeking an experienced AI Engineer ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

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All JobsApplied Scientist Machine Learning JobsInternship Applied Scientist Machine Learning JobsInternship Applied Scientist Machine Learning Jobs in Michigan

Internship Applied Scientist Machine Learning information

What types of projects do Internship Applied Scientists in Machine Learning typically work on, and how do they contribute to the team's goals?

Internship Applied Scientists in Machine Learning often collaborate with multidisciplinary teams to tackle real-world problems using data-driven approaches. Typical projects might include developing and fine-tuning machine learning models, conducting experiments to validate hypotheses, or assisting in the deployment of algorithms into production systems. Interns are expected to contribute fresh perspectives, help with data preprocessing, and perform thorough model evaluations. Through these projects, interns gain hands-on experience while directly supporting the team's research and product development objectives.

What is the difference between Internship Applied Scientist Machine Learning vs Internship Data Scientist?

AspectInternship Applied Scientist Machine LearningInternship Data Scientist
Required CredentialsRelevant degrees in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegrees in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentResearch and development teams, focus on ML model developmentBusiness teams, focus on data analysis and insights
Employer & Industry UsageTech companies, AI-focused organizationsVarious industries including tech, finance, healthcare
Comparison Search IntentUnderstanding roles in ML research and developmentUnderstanding data analysis and business insights roles

Internship Applied Scientist Machine Learning roles focus on developing and applying machine learning models, often in research settings. In contrast, Internship Data Scientist positions emphasize analyzing data to generate insights for business decisions. Both roles require strong analytical skills and relevant educational backgrounds, but they differ in their primary focus and work environment.

What are the key skills and qualifications needed to thrive as an Internship Applied Scientist in Machine Learning, and why are they important?

To thrive as an Internship Applied Scientist in Machine Learning, you need a solid background in mathematics, statistics, and computer science, often supported by coursework or research experience in machine learning and data analysis. Familiarity with tools such as Python, TensorFlow, PyTorch, and experience working with large datasets are highly valued, along with knowledge of version control systems like Git. Strong problem-solving skills, curiosity, and the ability to communicate complex concepts clearly set top candidates apart. These competencies are crucial for effectively designing, implementing, and presenting machine learning solutions that address real-world challenges.

What does an Internship Applied Scientist in Machine Learning do?

An Internship Applied Scientist in Machine Learning works on real-world projects involving the design, development, and evaluation of machine learning models and algorithms. Their responsibilities typically include data analysis, building predictive models, experimenting with new techniques, and collaborating with engineers and researchers to solve complex problems. Interns gain hands-on experience with tools like Python, TensorFlow, or PyTorch, and contribute to advancing the company's AI capabilities. The role requires a strong foundation in mathematics, statistics, and computer science, as well as the ability to communicate findings to both technical and non-technical stakeholders.
What are the most commonly searched types of Applied Scientist Machine Learning jobs in Michigan? The most popular types of Applied Scientist Machine Learning jobs in Michigan are:
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Internship Applied Scientist Machine Learning jobs near you
Data Science Analyst / Data Scientist

Data Science Analyst / Data Scientist

Resource Point LLC

Detroit, MI • On-site

Contractor

Posted 9 days ago

Job description

Job Title: Senior Data Science Analyst

Location: Detroit, MI

Duration: 6 months contract

Job Description:

Department: Analytics COE

  • Under minimal supervision, responsible for solving complex business cases by understanding problem statements and conditions to generate workable hypotheses and solve by creating data driven models.
  • Develops complex models by utilizing statistical, algorithmic and visualization techniques and provides insights for cross-functional teams.

Required Skills and Experience:

  • Proven experience designing and developing advanced analytics and AI solutions using machine learning and deep learning techniques, including natural language processing (NLP).
  • Hands-on experience working with big data and cloud platforms (AWS preferred, Databricks plus), along with strong software development skills and adherence to best coding practices.
  • Strong communication skills, with the ability to clearly explain technical concepts to project leads and present insights effectively to non-technical audiences. 
  • Programming: Proficient in Python
  • Ability to translate complex business problems into analytical solutions, decompose them into actionable components, and build end-to-end data-driven solutions from the ground up.
  • Experience developing custom NLP applications using deep learning models, including transformer-based architectures (e.g., large language models, or LLMs).
  • Familiarity with rapid prototyping and demo creation using frameworks such as Streamlit.
  • Prior experience in the healthcare industry preferred.

Education/Certifications:

  • Bachelor’s degree in data science, Machine Learning, Computer Science, Applied mathematics, Statistics, Mechanical Engineering, Operations Research, or related field is required.
  • Masters in related field is preferred.