Data Scientist [Multiple Positions Available]

JPMorganChase • Plano, TX

5d • Onsite

About The Position

Drive the design, build, and implementation of end-to-end data products that systemically publish actionable insights to commercial bankers and their clients. Create product recommendation models for core banking products utilizing internal and external data to inform modeling approaches, produce prototypes, and implement into production. Perform analytics exploring the relationship of companies, products, and banker engagement to inform the product roadmap and top-down decision-making. Drive collaboration across information architecture, data engineering, applied science, business intelligence, product management, data architecture, software engineering, and business analytics to bring products to market and architect data-driven solutions. Guide discovery of new data science solutions, collaborating directly with business and tech leaders across banking, product management, engineering, and applied science. Drive data migrations from legacy banking platforms to new platforms to improve system performance and reliability. Adopt new technologies and establish best practices to keep analytical solutions current, detect, diagnose, correct, and simplify problems. Explain data problems and solutions to business leaders across multiple levels of the banking organization.

Requirements

• Master's degree in Business Analytics, Data Science, Statistics, or related field of study
• Two (2) years of experience in the job offered or as Data Scientist, Business Analyst, Data Consultant, or related occupation.
• Two (2) years of experience in creating and maintaining models consisting of banking, financial, and external data sources
• Two (2) years of experience in building insights ecosystems to standardize disparate analytics into a single business-facing feed of insights
• Two (2) years of experience in designing and implementing scalable AI/ML models in large-scale enterprise environments using Databricks and MLflow
• Two (2) years of experience in developing end-to-end pipelines, managing model versioning, and enabling automated deployment and monitoring for production readiness
• Two (2) years of experience in defining business requirements and translating them into technical solutions
• Two (2) years of experience in optimizing model training and inference workflows for efficiency and scalability
• Two (2) years of experience in applying multivariate statistical methods including principal component analysis (PCA), linear discriminant analysis (LDA), and cluster analysis to uncover data patterns, reduce dimensionality, and inform feature engineering
• Two (2) years of experience in performing data preprocessing, outlier detection, and variable selection
• Two (2) years of experience in conducting exploratory data analysis to identify relationships and trends in high-dimensional datasets
• Two (2) years of experience in implementing feature selection and transformation techniques to improve model performance
• Two (2) years of experience in developing quantitative models to extract insights from enterprise datasets, including time series analysis, predictive modeling, and risk assessment
• Two (2) years of experience in validating model assumptions and assessing statistical significance of results
• Two (2) years of experience in communicating findings and recommendations to stakeholders through visualizations and reports
• Two (2) years of experience in building supervised machine learning models using techniques including regression, classification, and experimental design with model validation, hyperparameter tuning, and performance assessment using accuracy, precision, recall, and ROC-AUC metrics
• Two (2) years of experience in designing and executing controlled experiments using A/B testing to evaluate model impact
• Two (2) years of experience in implementing cross-validation and regularization methods to prevent overfitting and improve generalizability
• Two (2) years of experience in developing AI and ML models in a large-scale enterprise using clustering, decision trees, random forest, support vector machines, ensemble methods, boosting, neural networks, TensorFlow, and NLTK
• Two (2) years of experience in driving end-to-end project management using reproducible runs to deploy models into production.

Responsibilities

• Drive the design, build, and implementation of end-to-end data products that systemically publish actionable insights to commercial bankers and their clients.
• Create product recommendation models for core banking products utilizing internal and external data to inform modeling approaches, produce prototypes, and implement into production.
• Perform analytics exploring the relationship of companies, products, and banker engagement to inform the product roadmap and top-down decision-making.
• Drive collaboration across information architecture, data engineering, applied science, business intelligence, product management, data architecture, software engineering, and business analytics to bring products to market and architect data-driven solutions.
• Guide discovery of new data science solutions, collaborating directly with business and tech leaders across banking, product management, engineering, and applied science.
• Drive data migrations from legacy banking platforms to new platforms to improve system performance and reliability.
• Adopt new technologies and establish best practices to keep analytical solutions current, detect, diagnose, correct, and simplify problems.
• Explain data problems and solutions to business leaders across multiple levels of the banking organization.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level