The role involves assisting in the collection, cleaning, and preprocessing of large datasets to ensure data integrity. The individual will conduct exploratory data analysis to identify trends and patterns while supporting the creation of features for machine learning models. Responsibilities include helping to build, train, and evaluate basic machine learning models under supervision. The analyst will maintain documentation of analytical findings and assist in preparing reports and visualizations. Additionally, the role involves developing search techniques, semantic search, and solutions using RAG frameworks and agentic frameworks.

Candidates must possess a Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Economics, or Engineering. Essential technical requirements include foundational knowledge in Python or R, basic SQL skills, and a solid understanding of statistical concepts and machine learning algorithms. Applicants should also be familiar with data visualization tools and demonstrate strong problem-solving, communication, and teamwork abilities. Preferred qualifications include experience with version control systems, cloud platforms, big data technologies, and participation in data science projects or bootcamps.