Data Scientist [Multiple Positions Available]

JPMorgan Chase & Co.•Plano, TX

28d•Onsite

About The Position

Duties: Build advanced machine learning and NLP models to address complex business challenges and enhance product capabilities. Design and implement creative software solutions that integrate machine learning models into real-world business flows, leveraging programming languages for production-quality code development. Collaborate with product managers and partner teams to translate business requirements into technical specifications and designs, incorporating state-of-the-art machine learning models. Develop scalable APIs using cloud services, ensuring secure and high-quality production code that meets industry standards. Utilize CI/CD tools and containerization methodologies to deploy code into production environments, automating processes to enhance operational stability and efficiency. Facilitate data pipelines to ensure seamless integration of machine learning models into existing systems. Drive evaluation sessions with external vendors, startups, and internal teams to assess architectural designs and technical credentials, ensuring applicability within existing information architecture. Drive firm-wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across various business areas, contributing to reusable code and components. Engage in independent research and exploration of new machine learning methods to stay abreast of advancements and apply them to complex business- critical problems. Implement statistical analysis and testing to validate machine learning models and ensure their effectiveness in solving business problems, providing insights into data-driven decision-making processes. Develop and utilize knowledge graphs to enhance data connectivity and semantic understanding, enabling advanced data analytics and solving problems such as fraud detection.

Requirements

Master's degree in Data Science, Machine Learning, Computer Science, or related field of study plus 3 years of experience in the job offered or as Data Scientist, Software Engineer, Machine Learning Engineer, Research Engineer, or related occupation. The employer will alternatively accept a Bachelor's degree in Data Science, Machine Learning, Computer Science, or related field of study plus 5 years in the job offered or as Data Scientist, Software Engineer, Machine Learning Engineer, Research Engineer, or related occupation.
Applying statistical methods and probability theory for effective hypothesis and A/B testing to enhance machine learning models and business decisions
Designing and developing machine learning models to address real-world business challenges using structured and unstructured data
Leveraging Python for machine learning model development, training, and inference using a rich library ecosystem
Integrating knowledge graphs with ML models to infer data relationships to enhance semantic understanding and AI decision-making
Building backend services using Java, and integrating solutions into business systems with multi-threading and multiprocessing for performance and security
Streamlining software development and deployment using Jenkins for CI/CD pipelines
Containerizing ML applications using Docker for consistent, scalable deployment
Deploying ML models with AWS services such as SageMaker leveraging AWS infrastructure for data storage, processing, and hosting
Employing Informatica for data integration and ETL processes, ensuring quality data management for timely analysis
Automating and scheduling workflows using Autosys for efficient machine learning and data pipeline execution
Providing modular, scalable software solutions using Microservices architecture integrating ML models into business workflows.

Responsibilities

Build advanced machine learning and NLP models to address complex business challenges and enhance product capabilities.
Design and implement creative software solutions that integrate machine learning models into real-world business flows, leveraging programming languages for production-quality code development.
Collaborate with product managers and partner teams to translate business requirements into technical specifications and designs, incorporating state-of-the-art machine learning models.
Develop scalable APIs using cloud services, ensuring secure and high-quality production code that meets industry standards.
Utilize CI/CD tools and containerization methodologies to deploy code into production environments, automating processes to enhance operational stability and efficiency.
Facilitate data pipelines to ensure seamless integration of machine learning models into existing systems.
Drive evaluation sessions with external vendors, startups, and internal teams to assess architectural designs and technical credentials, ensuring applicability within existing information architecture.
Drive firm-wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across various business areas, contributing to reusable code and components.
Engage in independent research and exploration of new machine learning methods to stay abreast of advancements and apply them to complex business- critical problems.
Implement statistical analysis and testing to validate machine learning models and ensure their effectiveness in solving business problems, providing insights into data-driven decision-making processes.
Develop and utilize knowledge graphs to enhance data connectivity and semantic understanding, enabling advanced data analytics and solving problems such as fraud detection.