Data Scientist

- 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