Applied AI ML lead at JPMorgan Chase in McLean, VA

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. You will be part of a world-class global Cybersecurity team and work along side technologists and innovators who work every day to protect the assets we manage.

As a member of the Cyber Technology and Controls (CTC) Cyber Operations 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. 

Preferred candidates will have a strong working knowledge of standard workflow 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.

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

• Solid knowledge and extensive experience in Python 
• Experience with anomaly detection using autoencoders or other techniques
• 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
• Model development frameworks such PyTorch and Scikit-Learn
• Experience with classification and regression trees (Random Forest, XGBoost, AdaBoost) 
• Possess the ability to explain model selection, model interpretability and performance metrics verbally and in writing

• Bachelors Degree in Data Science, Mathematics, Statistics, Econometrics or Computer Science and 3+ years data-science experience (Exploratory Data Analysis, statistical analysis and reporting results.

   

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
• Working knowledge of Large Language Models (LLM), NLP, Embeddings and Retrieval Augmented Generation (RAG)
• Working knowledge of Responsible AI, model fairness, and reliability and safety

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