Work with large and complex financial datasets to develop end-to-end data science solutions for pricing various financial products, dynamic campaign optimization, and customer personalization.
Conduct research and literature review to assess and evaluate trade-offs between different quantitative algorithms and models.
Implement and train AI/ML models and optimize algorithm efficiency (GPU distributed computing, concurrent programming)
Refactor and document code into reusable libraries/ APIs/ tools, deploy machine learning ecosystems, and perform sub-system integration as required.
Integrate solutions into enterprise MLOps ecosystem and automate CI/CD pipelines for model lifecycle maintenance and monitoring.
Requirements:
Good understanding of the data science production life cycle with demonstrable experience working with structured, semi-structured and unstructured data.
Excellent software skills (Python, SQL variants) and knowledge in design patterns, code optimization, object-oriented programming.
Experience applying quantitative and machine learning algorithms for pricing and marketing.
Demonstrable expertise in some of the following domains - econometrics, statistical modelling, time-series analysis, signal processing, reinforcement learning, estimating causal relationships/ counterfactual effects, dynamic pricing.
Solid understanding of foreign exchange markets, including knowledge of currency pairs, market dynamics, and key drivers.
Hands-on experience in designing and executing digital campaigns and experimentation (A/B, Multivariate, Bandits, Sequential, quasi-experiments), evaluation methodologies (DiD variants), and conducting experiments to optimize campaign performance.
