Internship - Data Scientist

The AI/Data Science Intern will support both (1) sustenance and continuous improvement of existing production AI applications and (2) development of new AI use cases across multiple problem types (e.g., computer vision, predictive modeling, anomaly detection, optimization, and GenAI where applicable).
Your Role
Key responsibilities in your new role

• 1. Production Use Case Sustenance / Performance Monitoring
  - Establish and maintain monitoring metrics for deployed AI models to ensure accuracy, reliability, and stability in production. - Identify performance deviations, investigate root causes, and implement corrective actions (e.g., re-training, calibration, fine-tuning).
• 2. New Use Case Development (Exploration to Prototype/Pilot Support)
  - Support ideation, data exploration, feasibility assessment, and prototyping for new AI use cases aligned with business needs.
  - Assist in selecting suitable approaches (ML/DL/GenAI) and conducting experiments to validate technical feasibility and KPI impact.
  - Contribute to transitioning prototypes toward pilot/production readiness through reproducible workflows and documentation.
• 3. AI Workflow and Data Pipeline Enhancement
  - Refine datasets and improve data preprocessing pipelines to ensure robust and repeatable model performance.
  - Support data quality checks, feature engineering, augmentation (where applicable), and labeling/ground-truth alignment.
• 4. Model Evaluation, Benchmarking, and Validation
  - Benchmark models against KPIs and validate model outputs to ensure adherence to business requirements and risk mitigation.
  - Document evaluation outcomes, assumptions, limitations, and recommendations.
• 5. Collaboration and Knowledge Sharing
  - Work closely with cross-functional teams to sustain current AI applications and align new developments with business needs.
  - Contribute to internal documentation, demos, and knowledge-sharing to improve team efficiency and reuse.
• 6. Research and Continuous Improvement
  - Explore and propose tools, techniques, and emerging best practices to improve model performance, maintainability, and development efficiency.
  - Where appropriate, apply relevant advancements to existing applications and new use case development.
• 7. Deployment and Scaling Support
  - Support deployment readiness and scaling of AI models across processes and sites, including workflow standardization and execution support.

Internship Learning Outcomes

• The intern will gain hands-on experience in monitoring, maintaining, fine-tuning, and optimizing deployed models, improving data pipelines, evaluating models against business KPIs, and contributing to deployment and scaling across processes and sites.

Your Profile
Qualifications and skills to help you succeed

• On track to attaining Masters/Bachelors in Business Analyst, Computer Science, and Data Science related roles.
• Basic to intermediate knowledge of machine learning fundamentals (e.g., supervised learning, model evaluation, overfitting, cross-validation).
• Proficiency in Python.
• Working knowledge of deep learning frameworks: TensorFlow and/or PyTorch. - Basic knowledge of SQL for data extraction and analysis.
• Familiarity with Git or similar version control systems.
• Exposure to compute environments involving GPU usage and/or HPC concepts (e.g., job scheduling, resource constraints, performance considerations).
• Awareness of containerization and orchestration concepts, including Kubernetes (basic understanding acceptable).
• Preferred Internship Period: Jun 26 - Dec 26

Contact:
Hillary Woo

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