Junior ML Analyst (STEM OPT) 1. Model Development & Experimentation Algorithm Implementation: Apply advanced theoretical knowledge from CSCE 5215 (Machine Learning) to select, implement, and train supervised and unsupervised learning models. Hyperparameter Tuning: Use Empirical Analysis (CSCE 5310) to perform systematic grid searches and Bayesian optimization, improving model metrics such as F1-score and Mean Squared Error. Prototyping Agentic Systems: Research and develop autonomous agent workflows that leverage Large Language Models (LLMs) to automate complex business requirements gathered from stakeholders. 2. Data Engineering & Feature Analysis Pipeline Construction: Design and maintain robust data pipelines using Python and SQL, ensuring data integrity for high-volume training sets as mastered in CSCE 5350 (Fundamentals of DB). Feature Engineering: Perform exploratory data analysis (EDA) on Big Data (CSCE 5300) to identify key variables, handle missing data, and perform dimensionality reduction to enhance model performance. Data Modeling & Transformation: Utilize INFO 5707 (Data Modeling) principles to structure non-relational and relational data for efficient ingestion into ML frameworks like PyTorch or Scikit-learn. 3. Deployment & MLOps Support Full-Stack Integration: Collaborate with the engineering team to integrate ML models into web applications using Vite and React, ensuring low-latency inference for end-users. Secure AI Deployment: Work with the security team to implement Secure E-Commerce (CSCE 5560) protocols for AI-driven transaction monitoring and fraud detection systems. Model Monitoring: Build dashboards to track model drift and performance over time, ensuring that production models maintain their accuracy against evolving datasets. 4. Technical Research & Documentation STEM OPT Milestone Tracking: Maintain comprehensive technical logs and training documentation as required by the Form I-983, bridging academic concepts with industrial application. Cross-Functional Translation: Translate complex algorithmic results into actionable business insights for non-technical leadership at Navonsys. Algorithm Auditing: Conduct regular code reviews and audits for computational efficiency, applying principles from CSCE 5150 (Computer Algorithms). Key Technical Competencies Programming: Python (Pandas, NumPy, Scikit-learn), SQL, TypeScript. Frameworks: Vite, React, Hono (for lightweight ML APIs). Environment: Docker for containerized model deployment, Git for version control.