Responsibilities:
1. Drive analytic projects for all pillars of the bank’s treasury and markets department, such as bond analytics, news generation FX trading signals, product recommendation and customer hyper-personalization.
2. Conduct literature review and evaluate trade-offs between different quantitative algorithms and AI/ML models.
3. Utilize advanced statistical and machine learning methods to derive insights and generate predictions from large volumes of transactional, operational and customer data.
4. Implement and train AI/ ML models and optimize algorithm efficiency (GPU distributed computing, concurrent programming)
5. Write and refactor code into reusable libraries/ APIs/ tools, deploy machine learning ecosystems/ pipelines and perform sub-system integration as required.
6. Integrate solutions into enterprise MLOps ecosystem and automate CI/CD pipelines for model lifecycle maintenance and monitoring.
Required Experience;
1. Bachelor/ masters in a quantitative discipline, such as Mathematics/Statistics, Business Analytics, Computer Science, Engineering, or equivalent experience.
2. Good understanding of the data science production life cycle with demonstrable experience working with structured, semi-structured and unstructured data.
3. Excellent software skills (Python, SQL variants) and knowledge in design patterns, code optimization, object-oriented programming.
4. Demonstrable understanding and application of varied AI/ML concepts and models, their mathematical underpinnings, and trade-offs
5. Practical experience using quantitative and statistical techniques to solve business problems such as hypothesis testing, regression, causal and correlation analysis, etc.
6. Excellent verbal and written communication skills to explain insights from technical work to non-technical audience through presentation or other means.
Good to Have (At least 2 or more)
1. Familiar with Linux OS, Openshift, Kubernetes for container-based deployment.
2. Experience with language models, RAG concepts, opensource generative AI (GenAI) frameworks and prompt engineering principles.
3. Experience using open-source computer vision and Document AI models and concepts (Table recognition, handwriting recognition, key information extraction, signature detection, image understanding)
4. Solid understanding of deep-learning model architectures and experience building custom architectures.
5. Experience building data models, writing complex queries and using business intelligence/ visualization tools (Tableau)
6. Understanding of the MLOps ecosystem and experience building systems to maintain the complete model lifecycle.
7. Expert knowledge in time series analysis, statistical signal processing and related modelling techniques.
8. Deep understanding of pricing, modelling, booking, and risk across diverse products and markets.
