Preferred candidates will have a strong working knowledge of common workflows for data analysis, data preparation and model development. They must have a working knowledge of data analysis and manipulation tools, statistics (e.g. statistical distributions and probability) and have experience with applying supervised and unsupervised learning models to solve well defined problems. They should possess the ability to develop statistical and Deep Learning models, measure their outcomes and be able to interpret them for business stakeholders. Candidates should have a working knowledge of Generative AI models, transformer architectures and when to apply these tools and techniques.

As a Lead Data Scientist at JPMorgan Chase within the Cybersecurity and Technology Controls line of business, you are an integral part of team that works to deliver Machine Learning solutions that satisfy pre-defined functional and user requirements with the added dimension of detection and prevention of misuse, circumvention, and malicious behavior. As a core technical contributor, you are responsible for carrying out critical technology solutions with tamper-proof, audit defensible methods across multiple technical areas within various business functions. 

Job Responsibilities

 

Works with stakeholders and business leaders to understand security needs and recommend business modifications during periods of vulnerability. Work with cybersecurity engineers and data engineers to acquire data that addresses each use case (fraud, anomaly detection, Cyber threats). Perform Exploratory Data Analysis on datasets and communicate results to stakeholders. Select statistical or Deep Learning models that are best positioned to achieve business results. Perform feature engineering or hyperparameter tuning to optimize model performance. Perform model governance activities for model interpretability, testability and results. Executes creative security solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions and break down technical problems. Develops secure and high-quality production code and reviews and debugs code written by others. Minimizes security vulnerabilities by following industry insights and governmental regulations to continuously evolve security protocols, including creating processes to determine the effectiveness of current controls. 

 Required qualifications, capabilities, and skills

Formal training or certification on security engineering concepts and 5+ years applied experience.  Advanced in one or more programming languages Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security Working knowledge of probability, statistics and statistical distributions and their applicability to use cases and the ability to perform Exploratory Data Analysis using Jupyter or SageMaker Notebooks Proficient in Pandas, SQL and Data Visualization tools such as Matplotlib, Seaborn or Plotly Working knowledge of Scikit-Learn for development of classification, regression and clustering models and Deep Learning frameworks such as PyTorch Experience with feature engineering complex datasets. Possess the ability to explain model selection, model interpretability and performance metrics verbally and in writing. 

Preferred qualifications, capabilities, and skills 

 

Bachelor’s degree in Data Science, Mathematics, Statistics, Econometrics or Computer Science and 7+ years data-science experience (Exploratory Data Analysis, statistical analysis and reporting results). Experience with Knowledge Graphs, graph analytics and graph databases a plus. Working knowledge of Large Language Models (LLM), NLP, Embedding Models and Vector Databases. Experience with Retrieval Augmented Generation (RAG) applications and the frameworks used to create them such as Langchain or Llamaindex. Experience with AI Agent frameworks such as Google ADK and Langraph  Experience deploying Statistical or Machine Learning models via AWS SageMaker in a production setting. Working knowledge of Responsible AI, model fairness, and reliability and safety.