Cybersecurity is one of the highest growth areas within JPMorgan and has a unique opportunity to develop and deploy Machine Learning solutions that support Cyber Operations. A successful candidate must be comfortable working independently, have an understanding of data analysis, statistics, data engineering and the ability to develop predictive models that meet defined business outcomes. 

As a Lead Security Engineer at JPMorgan Chase within the Cyber Technology and Controls Product team, you will be part of a highly motivated team that focuses on analyzing data and creating and delivering Machine Learning solutions that will protect the firm from a variety of cyber-related threats.

Job responsibilities :

• Engage with cybersecurity domain experts to understand business goals and use cases related to using real-world data to solve business problems
• 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
• Document measurements required to detect model or data drift in a Production setting
• Perform model governance activities for model interpretability, testability and results

Required qualifications, capabilities, and skills :

• Formal training or certification on Data Science concepts and 5 years applied experience.
• 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 probability, statistics and statistical distributions and their applicability to use cases
• Working knowledge of Scikit-Learn for development of classification, regression and clustering models
• Deep Learning frameworks such as Keras, Tensorflow or PyTorch
• Experience with classification and regression trees (Random Forest, XGBoost, AdaBoost) 
• 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 :

• Experience deploying Statistical or Machine Learning models in a production setting
• Experience with model monitoring and understanding data quality issues
• Experience creating synthetic datasets 
• Development of REST APIs using tools such as Flask or FastAPI
• Working knowledge of Responsible AI, model fairness, and reliability and safety