Data Scientist - Applied Ai/ml

JPMorgan Chase JPMorgan Chase · Banking · Metro Manila, National Capital Region, Philippines · Consumer & Community Banking

This role focuses on designing, developing, and deploying predictive models, advanced analytics, and large language model agentic solutions within a financial services context. The individual will build reusable services, integrate agentic workflows with retrieval-augmented generation and tool/function calling, and own the end-to-end model delivery lifecycle, including production pipelines and responsible AI practices.

What you'd actually do

  1. Design, develop, and deploy predictive machine learning, advanced analytics, and generative AI solutions for complex business problems
  2. Build and integrate agentic workflows into end-to-end business processes, including retrieval-augmented generation, tool or function calling, routing, and structured outputs
  3. Prototype AI-enabled approaches quickly and harden successful prototypes into reusable, production-ready services with measurable outcomes
  4. Own end-to-end model delivery, including dataset preparation, feature engineering, training, validation, evaluation, deployment, and iteration
  5. Design, deploy, and operate production pipelines and services (batch and real-time), including monitoring, retraining strategies, and reliability and cost improvements

Skills

Required

  • Python proficiency for data analysis, modeling, and production implementation
  • Experience building, evaluating, and deploying predictive models using common libraries (PyTorch, TensorFlow, scikit-learn)
  • Experience building and deploying large language model workflows with retrieval-augmented generation and tool or function calling
  • Experience defining and using an evaluation approach for large language model solutions
  • Experience operating production pipelines or services (versioning, continuous delivery, monitoring, alerting, incident hygiene)
  • Working knowledge of cloud and/or containerized environments (AWS, Azure, GCP, Kubernetes)

Nice to have

  • Master’s degree or doctorate in a quantitative field
  • Publications, patents, or meaningful open-source contributions related to machine learning or generative AI
  • Experience scaling agentic systems across multiple use cases, including mature evaluation practices and quality dashboards
  • Experience implementing guardrail patterns and operating controls for generative AI features in production
  • Experience with large-scale data processing and cloud data services, and exposure to performance optimization for model serving
  • Experience in financial services or other regulated industries
  • Experience with specialized domains such as search and ranking, recommender systems, graph machine learning, or knowledge graphs

What the JD emphasized

  • three years of experience delivering end-to-end AI or machine learning solutions from prototype to production
  • Experience building and deploying large language model workflows that include retrieval-augmented generation and tool or function calling
  • Experience defining and using an evaluation approach for large language model solutions
  • Experience operating production pipelines or services
  • Experience in financial services or other regulated industries

Other signals

  • design and deploy predictive models
  • large language model agentic solutions
  • build reusable services
  • prototype AI-enabled approaches
  • own end-to-end model delivery
  • design, deploy, and operate production pipelines
  • apply responsible AI and governance-aligned practices
  • contribute reusable patterns