Required Experience Minimum 14+ years of experience in data science designing and deploying statistical modelling with at least 6 years of hands-on experience in Financial/Investment / Asset Management / Fixed Income. Has experience with data science, statistical modeling and time series analysis of financial markets data, Fixed Income Assets and/or Investment Banking.
Has experience in Gen AI , Language Model etc. Worked for asset management firms in roles aligned to the front office. Has advanced knowledge of python and/or R Asset Allocation, Research, quat engineering Broad experience with hands-on modelling and DS lifecycle activities, particularly in Python, and ability to communicate at a high level with DS and non-technical leaders Knowledge of graphical packages such as plotly or gg plot Excellent understanding of Statistical modeling, Machine Learning (Supervised, Unsupervised, Recommendation engines, Optimization etc Deep Learning (RNN, CNN, LSTM, Auto encoders, GANS etc, Natural Language Processing techniques and algorithms Strong coding experience in Python, Pyspark, Keras/tensorflow Experience with SQL & at least one NoSQL databases Experience of working on any of cloud platform (AWS, Azure, IBM etc Work closely with data engineers to source, analyze and engineer features, collaborate with fellow data scientists to develop, test and deploy Machine Learning models Exposure to creation of data pipelines to engineer apps deployment (CI/CD) across platforms and environments and working with ML pipelines Demonstrate analytical and problem-solving skills, particularly those that apply to the big data environment
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