JPMorgan Chase is seeking a Data Scientist Vice President to join the Compliance, Conduct Operational Risk (CCOR) Data Analytics team. The role involves developing and deploying AI/Gen AI/ML models to address risks and improve business operations within a regulated environment. The core problem is to leverage advanced data analytics and AI techniques to anticipate and mitigate risks, ensuring the firm's resilience and responsible growth.
Requirements
- 6+ years of related experience in Python, R or Scala
- Demonstrable theoretical and application knowledge of AL/ ML, Gen AI and Statistical Models
- Demonstrable hands-on experience with Transformer or other deep learning architectures in real applications
- Demonstrable hands-on experience with using or fine tuning multimodal LLM in real business applications with scale and performance
- Demonstrable hands-on experience and familiarity with any or all of the following packages, algorithms, and/or alternatives, including Graph Learning Packages : (NetworkX, Torch-Geometric, Graphframes, Graphistry),ML Packages (Pandas, Scikit-Learn, XGBoost, catboost, lightgbm, automl, Optuna, Hyperopt), Visualization Packages (Matplotlib, Seaborn, Geopandas), Algorithm (Ensemble Louvian / Hierarchical Clustering, Label Propagation, Connected Component Analysis, Graph Neural net (Graph Attention Network), Page Rank, Centrality Analysis, Tree based Analysis, Outlier Detection Methods, Zero Shot/ Few Shot learning)
- Demonstrable experience with graph analytics, graph-based learning, and graph representation/visualization
- Experience in graph Database: TigerGraph, Neo4j
Responsibilities
- Devising and developing Proofs of Concept (POCs) and deployable models using AI/Gen AI/ ML techniques, algorithms and other statistical and numerical methods.
- Extract and work with large volumes of data (both structured and unstructured) from multiple sources, transforming it into an analysis-ready format to develop the data pipeline.
- Independently formulate methodologies, and quantitative and analytical tasks, from business problems.
- Analyze complex/unstructured data to understand the business problem and use case
- Analyze business requirements, design, and develop appropriate methodology
- Develop deployable, scalable and effective models/ analytical methods as part of technology managed system or as a self-served application of a business user
- Prepare technical documentation of quantitative models for internal model risk and governance review
Other
- Work collaboratively and creatively with other data scientists, technology partners, risk professionals, model validation teams, etc.
- Experience with processes, controls and governance of a highly regulated environment
- Self-starter and strong influencing skills with strong communication skills
- Real life exposure to Agile SDLC, ModelOps and /Or Design Thinking is desirable.
- Experience in financial services industry especially in Operational Risk Management, Anti-Money Laundering & Know Your Customer, Trade Surveillance model development
