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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 ...

OpTech

Scientist-Research Engineer (ML)

Dearborn, MI · On-site

The ideal candidate will leverage data science, machine learning, physics-based modeling, and signal processing techniques to predict component degradation and estimate Remaining Useful Life (RUL ...

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 ...

Corning

$95K - $130K/yr

You will help translate data science prototypes into secure, scalable, and production-ready ... end machine learning pipelines covering data ingestion, preprocessing, training, validation ...

Varsity Tutors

Machine Learning Tutor

Kalamazoo, MI · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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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:
What are popular job titles related to Internship Applied Scientist Machine Learning jobs in Michigan? For Internship Applied Scientist Machine Learning jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Internship Applied Scientist Machine Learning jobs in Michigan look for? The top searched job categories for Internship Applied Scientist Machine Learning jobs in Michigan are:
What cities in Michigan are hiring for Internship Applied Scientist Machine Learning jobs? Cities in Michigan with the most Internship Applied Scientist Machine Learning job openings:
Internship Applied Scientist Machine Learning jobs near you

Data Science and Machine Learning Senior Associate

KYYBA Inc

Dearborn, MI • On-site

Full-time

Re-posted 29 days ago

Job description

Job Summary:
KYYBA Inc is a company seeking a Data Science and Machine Learning Senior Associate to leverage data science methodologies for predicting and extracting meaningful trends from raw data. The role involves designing and implementing data analysis and machine learning models to support data-driven decision-making.
Responsibilities:
• Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making
• Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making
• Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends
• Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation
• Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness
• Model Development: Design, develop, and deploy high-performance machine learning models (supervised, unsupervised, and reinforcement learning) to address business needs such as churn prediction, recommendation engines, or demand forecasting
• Experimental Design: Lead the design and analysis of large-scale experiments (A/B testing, multivariate testing) to validate hypotheses and measure the impact of product changes
• Feature Engineering: Architect and implement robust data pipelines and feature engineering processes to improve model accuracy and scalability
• Algorithm Optimization: Evaluate and refine existing algorithms to improve computational efficiency and predictive power
• Stakeholder Influence: Act as a strategic advisor to leadership, translating complex algorithmic outcomes into business-centric narratives that drive ROI
• Technical Leadership: Mentor junior data scientists and contribute to the team’s internal library of best practices, code standards, and research methodologies
• Collaboration with Engineering: Partner with ML Engineers and DevOps to integrate models into production systems, ensuring reliability and monitoring model drift.
Qualifications:
Required:
• 5+ years of experience in a Data Science role, with a proven track record of delivering models that impact business outcomes.
• Expert proficiency in Python (specifically libraries like Pandas, NumPy, Scikit-learn, SciPy) or R.
• Deep understanding of a broad range of ML techniques, including Gradient Boosting (XGBoost/LightGBM), Random Forests, GLMs, and Clustering.
• Ability to manipulate and extract data from complex, multi-terabyte distributed databases.
• Strong foundation in linear algebra, calculus, and advanced statistical inference.
• Experience with version control (Git) and writing clean, modular, and maintainable code.
• Master's Degree
Company:
Kyyba, Inc. Founded in 1998, the company is headquartered in Farmington Hills, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
KYYBA logo

About KYYBA

Sourced by ZipRecruiter

About Kyyba: Founded in 1998 and headquartered in Farmington Hills, MI, Kyyba has a global presence delivering high-quality resources and top-notch recruiting services, enabling businesses to effectively respond to organizational changes and technological advances. At Kyyba, the overall well-being of our employees and their families is important to us. We are proud of our work culture which embodies our core values; incorporating value, passion, excellence, empowerment, and happiness, creates a vibrant and productive atmosphere. We empower our employees with the resources, incentives, and flexibility that they need to support a healthy, balanced, and fulfilling career by providing many valuable benefits and a balanced compensation structure combined with career development.

Industry

Recruiting and staffing services

Company size

501 - 1,000 Employees

Headquarters location

Farmington Hills, MI, US

Year founded

1998

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