Applied AI ML [Multiple Positions Available]
This role at JPMorgan Chase focuses on applying advanced AI and ML techniques to derive actionable business insights from complex datasets, bridging statistical rigor with real-world financial problem-solving. Candidates will collaborate across teams to prototype solutions and present findings to senior leadership, emphasizing both technical depth and strategic impact.
Skills required
About this role
DESCRIPTION:
Duties: Apply statistical and machine learning methodologies, including causal inference methodologies, and perform advanced analytics to solve complex and multi-faceted business problems. Perform advanced quantitative and statistical analysis of large datasets to identify trends, patterns, and correlations that can be used to improve business performance. Generate deep insights through data analysis and understanding of business processes and turn insights into actionable recommendations using algorithms. Build and deploy prototype solutions to demonstrate ideas and prove concepts. Collaborate with others in the organization to develop new ideas and brainstorm potential solutions. Develop presentations to summarize and communicate key messages to senior management sponsors and colleagues.
QUALIFICATIONS:
Minimum education and experience required: PhD in Economics, Statistics, Mathematics, or related field of study plus 1 year of experience in the job offered or as Applied AI ML, Quantitative Analytics, or related occupation. The employer will alternatively accept a Master's degree in Economics, Statistics, Mathematics, or related field of study plus 3 years of experience in the job offered or as Applied AI ML, Quantitative Analytics, or related occupation.
Skills Required: This position requires experience with the following: Making predictions and inferences using analytical methods including generalized linear models, non-linear models, ML frameworks including tree-based models, time series, causal inference and causal estimation including Structural Equation Modeling and conditional likelihood; data interpolation for estimation and inference with cross sectional and panel data; Heterogeneous data management and programming using SQL, Matlab, R, and Python; writing technical documentation using Latex for review by regulators in line with expectations for academic journals.
Job Location: 8181 Communications Parkway, Plano, TX 75024.
Full-Time.