Data Scientist - Vice President

Job Description

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 know 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 a 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.

• Work with stakeholders and business leaders to understand security needs and recommend business modifications during periods of vulnerability.
• Collaborate 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.
• Execute 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.
• Develop secure and high‑quality production code and review and debug code written by others.
• Minimize security vulnerabilities by following industry insights and governmental regulations to continuously evolve security protocols, including creating processes to determine the effectiveness of current controls.

• Formal training or certification on security engineering concepts and 5+ years applied experience.
• Advanced proficiency 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, with the ability to perform Exploratory Data Analysis using Jupyter or Sage Maker notebooks.
• Proficiency 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 of complex datasets.
• Possess the ability to explain model selection, model interpretability and performance metrics verbally and in writing.

• 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.
• 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 Lang Chain or Llama Index.
• Experience with AI Agent frameworks such as Google ADK and Langraph.
• Experience deploying statistical or machine‑learning models via AWS Sage Maker in a production setting.
• Working knowledge of Responsible AI, model fairness, reliability…