- Apply AI techniques, including statistical analysis & modeling ML and DL algorithms, on complex prediction and NLP tasks in finance domain to enable data-driven decision making, increase business values and reduce operational costs
- Work on the latest Deep Learning architectures tweaking transformers and training DL models from scratch
- Write, debug and refine Ml and DL pipelines for efficient data preprocessing, model training, testing and error analysis. Write clean and production-ready codes to automate model (re-)training and deployment.
- Conduct in-depth analysis of subject-matter datasets through interactive visualization, data mining and statistical analysis
- Research and bring in the latest high-performant ML/DL concepts for finance-related problems
- Work on model explainability by experimenting and implementing state-of-the-art techniques
- Work with Data Engineers and Backend & Frontend Developers to integrate ML models into various enterprise products and solutions
- Work closely with various stakeholders to promote AI awareness and adoption
- Good knowledge of LLMs in training, finetuning, inferencing and building LLM-powered applications
- Must have experience with building and training neural networks from scratch in any of these areas: CV, NLP, RL, Time Series.
- Sound theoretical and practical knowledge in machine learning, deep learning and statistics
- Expert in Deep Learning frameworks (preferably Pytorch) and NLP techniques like Word/Sent/Doc2Vec, BERT, GPT, as well as related frameworks like spaCy, Huggingface Transformers, Gensim etc.
- Expert in Python and data science related tools like Sklearn, Pandas, Matplotlib, Jupyter, etc.
- Strong Experience with structured (SQL) and unstructured (NoSQL) databases.
- Knowledge of basic software development and system
- And most importantly, you must be a passionate data scientist who really cares about applying ML/DL on solving real-world problems and is involved in the end-to-end model life cycle
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