Temporary Intern - Artificial Intelligence/Machine Learning Engineer

Cotiviti

1d • $20 - $25 • Remote

About The Position

This internship offers a unique opportunity to work with cutting-edge machine learning algorithms and their integration into software applications. Under the guidance of experienced professionals, you will contribute to real-world projects, gain hands-on experience, and expand your knowledge in the exciting field of artificial intelligence and machine learning.

Requirements

• Pursuing a degree in Computer Science, Data Science, Machine Learning, or a related field.
• Strong understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, classification, regression, and clustering.
• Proficiency in at least one programming language commonly used in machine learning (Python preferred).
• Familiarity with machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or Keras.
• Experience with data manipulation and analysis using tools like pandas, NumPy, or MATLAB.
• Solid understanding of software development principles and practices.
• Excellent problem-solving skills and attention to detail.
• Critical Thinking: Ability to think critically and evaluate information objectively, considering different perspectives and potential implications before drawing conclusions or making recommendations.
• Attention to Detail: must have a keen eye for detail to ensure accuracy in data analysis, interpretation, and reporting.
• Quantitative Aptitude: Strong numerical skills are essential for conducting quantitative analysis, working with statistical methods and models, and manipulating data using mathematical operations.
• Data Interpretation: skilled in interpreting data visualizations, charts, graphs, and other forms of data presentation to extract meaningful insights and communicate findings effectively.
• Communication Skills: Effective communication skills are crucial for conveying complex technical concepts and insights to non-technical stakeholders clearly and understandably through written reports, presentations, and verbal discussions.
• Curiosity and Learning Agility: A strong desire to learn and explore new methodologies, techniques, and tools in the field of data analysis and insights generation is essential for staying current with industry trends and best practices.
• Resilience: The ability to handle pressure, adapt to changing priorities, and overcome setbacks is important in a fast-paced and sometimes ambiguous analytical environment.
• Ethical and Integrity: Upholding ethical standards and maintaining integrity in handling sensitive data and information is paramount for building trust and credibility in the insights provided
• Remaining in a stationary position, often standing or sitting for prolonged periods.
• Repeating motions that may include the wrists, hands and/or fingers.
• Must be able to provide a dedicated, secure work area.
• be able to provide high-speed internet access / connectivity and office setup and maintenance.

Responsibilities

• Assist in developing machine learning models for various applications, including but not limited to natural language processing, recommendation systems, and predictive analytics.
• Work on data preprocessing tasks such as data cleaning, feature engineering, and exploratory data analysis to prepare datasets for model training. Gain insights from data and identify patterns that can be leveraged for model development.
• Participate in model training processes using machine learning libraries such as TensorFlow, PyTorch, or scikit-learn. Conduct experiments to evaluate model performance, fine-tune hyperparameters, and optimize algorithms for accuracy and efficiency.
• Collaborate with software developers to integrate machine learning components into software applications and platforms. Understand software requirements and design machine learning solutions that meet performance, scalability, and usability criteria.
• Assist in testing machine learning models using validation techniques such as cross-validation and holdout validation. Evaluate model robustness, reliability, and generalization performance across different datasets and use cases.
• Document the development process, experimental results, and technical specifications of machine learning models. Prepare reports and presentations to communicate findings, insights, and recommendations to stakeholders.

What This Job Offers

• Career Level: Intern