Applied Artificial Intelligence

1 month ago

Mumbai, India JPMorgan Chase & Co. Full time

The Applied AI and Machine Learning team at JP Morgan Corporate Investment Bank combines cutting edge machine learning techniques with the company's unique data assets to optimize the business decisions we make. In this role, you will be part of our industry-leading data analytics team and advance the state-of-the-art in financial applications ranging from generating business intelligence to predictive models and automated decision making. Our work spans the Corporate Investment Banking, with exceptional opportunities in each line of business. The team covers a wide array of activities whether internally or externally focused: Controls, Compliance, Legal, Finance functions, Operations and Digital Investment Banking.

Job responsibilities:

Conduct end-to-end research such as formulating problems, gathering data, generating hypotheses, developing models and algorithms, conducting experiments, synthesizing results, building prototype applications, communicating the significance of research, and deploying the models following all relevant processes and governance.  Anticipate and consider risks when building machine learning solutions and prediction/classification systems.  Build and train production grade ML models on large-scale datasets to solve various business use cases for the firm.  Develop quantitative and machine learning models for markets data.  Develop methodologies for AI/ML model explainability.  Analyze trading data, patterns, and trends to detect potential market anomalies.  Work with Markets technology to identify and investigate data gaps and curate datasets for modeling.  Drive complex projects that require cross-organizational alignment and collaboration. 

Required qualification, capabilities, and skills 

Master’s, PhD, or equivalent degree program in mathematics, sciences, statistics, econometrics, engineering, financial engineering, computer science, or other quantitative fields  Expertise in Time Series Analysis, Forecasting, Statistics Markets domain knowledge and Experience working with equities, bonds, or commodities trading data Experience with SQL and Q query language for KDB Experience with quantitative methods in finance: Probabilistic modeling; Liquidity profiling; Reinforcement Learning; Optimization methods.  Experience with Network Analysis Experience with opensource models, machine learning APIs and computational packages.  Strong written and oral communication and interpersonal skills Programming experience with Python. Proficient in git version control 

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