SI and Machine Learning Intern

The candidate will be responsible for developing, implementing, and deploying super intelligence (previously artificial intelligence), machine learning, and data science solutions to address complex technical and analytical challenges. Key responsibilities include:

Data Science & Analytics: Conduct exploratory data analysis (EDA), statistical analysis, and hypothesis testing to extract meaningful insights from large, complex datasets; develop visualizations and dashboards to communicate findings

Predictive Modeling: Build predictive and prescriptive models to forecast trends, identify patterns, and support data-driven decision-making

Model Development: Design, train, and optimize machine learning models using various algorithms including supervised, unsupervised, and reinforcement learning techniques

Data Processing & Engineering: Perform data collection, cleaning, preprocessing, and feature engineering to prepare datasets for analysis and model training; develop data pipelines and workflows for efficient data management

Algorithm Implementation: Translate theoretical ML concepts into practical implementations using modern frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, pandas, NumPy)

Performance Optimization: Evaluate model performance using appropriate metrics, conduct hyperparameter tuning, and implement improvements to enhance accuracy, efficiency, and interpretability

Deployment & Integration: Deploy trained models and analytical solutions into production environments, integrate ML/data science solutions with existing systems, and ensure scalability and reliability

Research & Innovation: Stay current with emerging AI/ML/data science technologies, evaluate new methodologies, and recommend innovative approaches to solve challenging problems

Documentation & Communication: Document model architectures, analytical methodologies, and results; create clear visualizations and reports; communicate findings to technical and non-technical stakeholders through presentations and written deliverables

Collaboration: Work closely with cross-functional teams including software engineers, data engineers, domain experts, and business stakeholders to define requirements, understand problems, and deliver end-to-end solutions

Experimentation & A/B Testing: Design and execute experiments to validate hypotheses and measure the impact of data-driven interventions

The role requires strong analytical and statistical skills, programming proficiency (Python), data visualization capabilities, and the ability to work on multiple projects simultaneously while maintaining high-quality deliverables.

**Eligible Applicants:** Student
**Academic Level:** Undergraduate Freshman, Undergraduate Sophomore, Undergraduate Junior, Undergraduate Senior, Graduate Master's, Graduate Doctoral, Post Doctoral
**Activity Type:** In-person
**Citizenship:** US Citizen Only
**Type:** Internships