Machine Learning Intern

Model Development: Assist in designing, training, and fine-tuning machine learning models using Python and modern ML frameworks. Data Pipeline Maintenance: Help clean, preprocess, and analyze large datasets to ensure high-quality training inputs for our models. Performance Evaluation: Conduct experiments to evaluate model accuracy, precision, and recall; document findings and iterate based on performance metrics. Research & Implementation: Stay up-to-date with the latest developments in AI/ML and explore how they can be applied to solve specific fintech problems. Collaborative Deployment: Work with the engineering team to integrate model prototypes into our production environment. 🏢 Inspiring Office Environment When you come to the mylo office, you'll find creative workspaces, an open design that encourages team collaboration, and a well-equipped kitchen for your daily coffee breaks or snack moments. 👩‍💻 Learn from the Best You’ll be surrounded by experienced, passionate tech professionals who are ready to guide you. You'll learn fast, grow even faster, and gain real-world exposure that will help you take confident steps in your career. 🚀 Real Impact This isn’t a “watch-and-learn” kind of internship. You’ll be working on real projects, solving actual problems, and making a difference — from day one. Education: Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field. Technical Toolkit: Proficiency in Python (specifically libraries like Pandas, NumPy, Scikit-Learn, PyTorch, or TensorFlow). Back-end or full-stack knowledge. Statistical Foundation: A solid understanding of machine learning algorithms, statistics, and linear algebra. Data Fluent: Comfortable working with SQL to query and manipulate large datasets. Curious & Persistent: You love debugging complex issues and are comfortable with the iterative nature of ML model building. Nice to have Experience with cloud platforms (AWS, GCP, or Azure) and machine learning services (e.g., SageMaker, Vertex AI). Exposure to LLM and GenAI. Previous experience or passion projects in the fintech space (e.g., building a sentiment analysis tool for markets, or a personal expense classifier). Familiarity with version control systems like Git .