Data Scientist Analyst

As a Data Scientist Analyst in Asset and Wealth Management Risk within our AWM Risk team, you will play a senior role in our journey of innovation. Your primary focus will be on introducing and scaling up data-driven risk analytics solutions through the application of data science and machine learning techniques. Your contributions will transform our operations and business processes, enhancing the core value proposition of our system and expanding flexible analytical capabilities for the growth of Newton's platform. Your responsibilities in this role will include: - Collaborating with peers and stakeholders to identify use cases for Data Science that create value, utilizing your expertise in Computer Science, Statistics, Mathematics, and Data Science techniques for security and portfolio risk analytics. - Leading continuous improvements in AI/ML and statistical techniques used in data and analytics validation processes. - Designing and delivering scalable solutions using approved new technologies and tools, such as AI and LLMs, leveraging the citizen developer journey platform to enhance process efficiencies. - Contributing to the analysis of new and large data sets, adhering to best practice data modeling and architecture using big data platforms. - Contributing to the research and enhancement of the risk methodology for AWM Risk Analytics, covering sensitivity, stress, VaR, factor modeling, and Lending Value pricing for investment, counterparty, and liquidity risk. Required qualifications, capabilities, and skills: - Minimum 6 months of experience as a Data Scientist or in a related quantitative role. - Detail-oriented, technically proficient individual with the ability to multitask and work independently. - Excellent communication skills. - Strong understanding of statistics, applied AI/ML techniques, and a practical problem-solving mindset. - Knowledge in modular programming in SQL, Python, ML, AWS Sagemaker, TensorFlow, or similar tools. Preferred qualifications, capabilities, and skills include: - Practical experience in financial markets within Risk Management or a Front Office role. - Knowledge of asset pricing, VaR backtesting techniques, and model performance testing.,