This project is redefining investment intelligence with AI—bringing explainable, multilingual equity research to markets worldwide. Join us to build the ML systems that power actionable insights for investors everywhere.
What you’ll do:
— Design, train, and evaluate ML/DL models (with a strong emphasis on classical ML where appropriate) for ranking, prediction, and risk/quality signals.
— Productionize models end-to-end: data pipelines, feature stores, deployment, scaling, monitoring, and optimization.
— Collaborate with product, research, and engineering to translate financial problems into measurable ML solutions.
— Own experiment design, metrics, and A/B testing; communicate findings clearly to technical and non-technical stakeholders.
— Contribute to model governance, reproducibility, and documentation across the ML lifecycle.
What you’ll bring:
— MS or PhD in Computer Science, Data Science, AI, or a related quantitative field.
— 4+ years building and shipping ML/DL models in production (with depth in classical ML methods).
— Expert coding in Python; hands-on with PyTorch and/or TensorFlow and common DL libraries.
— Strong foundations in linear algebra, calculus, statistics, and probability.
— Solid grasp of algorithms and data structures.
— Proficiency with Pandas, scikit-learn, and the broader Python data stack.
— Experience with model deployment, optimization, scaling, and serving.
— Excellent problem-solving, analytical, and quantitative skills.
— Hands-on experience delivering solutions in the financial domain.
— Clear, concise communicator and a collaborative team player.
Nice to have:
— Research or applied experience in LLMs/NLP and modern machine learning.
— Work with multi-modal data (e.g., text, tabular/market data, images, audio).
— Familiarity with AWS or GCP for large-scale training/inference.
— Understanding of MLOps and production ML workflows (CI/CD for models, monitoring, model/data versioning).
— Background in information retrieval, knowledge graphs, or reasoning.
