Algorithm Development: Creating and testing new AI models and algorithms to solve specific problems or improve existing methods.
Data Engineering: Building data pipelines for model training and evaluation, including data collection, cleaning, preprocessing, and labeling, while contributing to better data management practices.
Model Testing & Evaluation: Designing and implementing rigorous testing frameworks to assess model performance and identify areas for improvement.
Collaboration: Working closely with team members to establish and refine research methodologies, promoting peer reviews, testing, and thorough documentation.
Research & Learning: Staying updated on the latest AI techniques and advancements, sharing insights, and actively bringing improvements to research processes.
Requirements & Skills:
PhD in artificial intelligence, machine learning, computer science, or a related field graduating between December 2024 and June 2025.
Strong foundation in AI concepts.
Strong knowledge of machine learning.
Solid technical and programming skills (Python, Java, GitHub).
Familiarity with machine learning frameworks (Spark, PyTorch, etc.).
Excellent analytical, problem-solving, and communication skills.
Deep interest in financial markets.
Experience with NLP tasks
Knowledge of TensorFlow or PyTorch.
Basic understanding of MLOps principles (monitoring, versioning, model serving).
