<p>JobID: 210717535</p> <p>Category: Predictive Science</p> <p>JobSchedule: Full time</p> <p>Posted Date: 2026-03-30T01:37:15+00:00</p> <p>JobShift:</p> <p>Base Pay/Salary: Jersey City,NJ $128,250.00-$195,000.00; New York,NY $128,250.00-$195,000.00</p> <p>Our team is responsible for pushing innovations in the Payments space, which is a cornerstone of JPMorgan Chase business. Payments are at the center of the global economy, connecting businesses and consumers around the world. In recent years, the payments industry has experienced rapid innovation with new technologies entering the market. Working in payments ML means being at the forefront of these changes and having the potential for a meaningful and lasting impact on global finance.</p> <p>As an Applied AI ML - Senior Associate within Commercial & Investment Bank AI ML Team, you will build production-grade models and services that power secure, scalable payment experiences. You will operate at the forefront of innovation-from generative AI to agentic systems-while collaborating with product, engineering, and operations to deliver measurable impact. If you are passionate about turning data and research into real customer outcomes, we would love to work with you.</p> <p>Job responsibilities</p> <ul> <li>Deliver machine learning applications from design and testing through containerization and deployment to cloud environments. </li><li>Build modular, scalable, and well-tested Python code using object-oriented patterns and version control workflows. </li><li>Partner with business stakeholders to deeply understand processes and identify high-value AI and machine learning opportunities. </li><li>Develop innovative solutions, including generative AI and agentic approaches, to solve complex operations challenges. </li><li>Architect and operate production machine learning services integrated with strategic systems for scale, reliability, and security. </li><li>Research and analyze datasets using statistical and machine learning techniques with strong experimental design and rigorous evaluation. </li><li>Define intrinsic and extrinsic evaluation methods aligned to business outcomes and customer impact. </li><li>Communicate findings, trade-offs, and model performance clearly to technical and non-technical audiences. </li><li>Establish reusable data science capabilities and tooling that accelerate multiple business use cases. </li><li>Document architectures, methods, and processes to promote transparency and reproducibility. </li><li>Collaborate across disciplines to drive data-led transformation and measurable business results. </li></ul> <p>Required qualifications, capabilities, and skills</p> <ul> <li>Master's degree in a quantitative field with at least 2 years of relevant experience, or Bachelor's degree with at least 3 years of relevant experience. </li><li>Demonstrated depth in machine learning fundamentals, data analysis, and experimental design. </li><li>Proven experience building and deploying data science and machine learning solutions to production at scale. </li><li>Strong Python development and debugging skills with production-grade, modular code practices. </li><li>Proficiency with version control and collaborative development workflows. </li><li>Familiarity with distributed computing patterns for model training, serving, and deployment. </li><li>Ability to design and interpret business-aligned evaluation metrics and testing frameworks. </li><li>Excellent problem solving, communication, and teamwork with strong client focus and attention to detail. </li><li>Experience with containerization and cloud deployment principles. </li><li>Track record of solution ideation and translating complex business problems into measurable machine learning solutions. </li></ul> <p>Preferred qualifications, capabilities, and skills</p> <ul> <li>Experience with natural language processing and generative AI toolkits, including agentic patterns. </li><li>Hands-on experience with PyTorch or TensorFlow and data science libraries such as scikit-learn, NumPy, SciPy, pandas, and statsmodels. </li><li>Experience deploying models on Amazon Web Services platforms such as SageMaker or Bedrock. </li><li>Practice integrating user feedback loops to refine and improve AI applications. </li></ul> <p>#CIBAppliedAI</p>