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Salary
≈ $23k – $47k per year (Estimated)
Location
In office (Bengaluru, Mumbai)
Seniority
Senior · 6+ years exp

Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Oct 12, 2026. Campbell Global scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Campbell Global brings more than three decades of experience and leadership to sustainable timberland and natural resource investment management.

Join the Cards Data & Analytics team at JPMorgan Chase, where we leverage data science, research, and business acumen to deliver impactful solutions.

As a Data Scientist Senior Associate within the Co-Brand Card Analytics team, you will be responsible to lead projects that integrate agentic workflows, business intelligence platforms, and advanced analytics solutions. You will own end-to-end delivery of segmentation models, strategic analyses, and AI-driven insights that inform partnership strategies and portfolio decisions. Utilizing tools such as Databricks, Python, Tableau and Alteryx, you will develop data pipelines and dashboards, prototype AI-driven solutions, and translate complex data into strategic recommendations for Co-Brand business partners.

Job Responsibilities

  • Be part of the Segmentation and Strategic Analytics team and drive analytics-driven business strategy and growth for Co-Brand Card partnerships.

    Learn our business and how JPMorgan delivers value for our customers and partners through Co-Brand credit card products.

  • Leverage your technical skillset, domain expertise, and curiosity to identify opportunities to help grow our Co-Brand Cards business through advanced segmentation and strategic analytics.
  • Build and rigorously test new data and AI driven insights, owning the end-to-end analytical lifecycle from hypothesis to production-ready deliverables.
  • Develop scalable frameworks for seamless AI model integration across business applications. Ensure solutions can adapt and scale to support evolving Co-Brand partnership needs.
  • Build and test AI agents. Iterate designs to enhance functionality and user experience. Conduct rigorous testing for reliability and mentor junior team members on best practices.
  • Use tools like Databricks, Python, Tableau and Alteryx to create data pipelines and dashboards. Support AI-driven insights and business recommendations for Co-Brand strategic initiatives.
  • Monitor AI model performance. Identify areas for enhancement. Implement updates to maintain quality and relevance. Drive continuous improvement of analytical methodologies.
  • Lead workstreams on agile teams to support data-driven decision-making, customer segmentation, and strategic partner relationship management.

Required Qualifications, Capabilities, and Skills

  • Degree in a scientific field (Computer Science, Engineering, Data Science, Statistics, Mathematics, etc.) with 6+ years of experience in AI/ML or Data Science.
  • Experience working in Card, Co-Brand, or other Financial Services verticals.
  • Strong understanding of AI models, including large language model (LLM) capabilities and limitations, and experience applying them to business problems.
  • Proven experience with statistical analysis, data-driven decision-making, customer segmentation, and pattern identification.
  • Strong creative problem-solving skills with the ability to translate complex analytical findings into actionable business recommendations.
  • Proficiency in Python and SQL with demonstrated ability to build production-quality analytical solutions.
  • Experience with cloud platforms such as AWS, GCP, or Azure, and SDLC concepts.
  • Strong communication and collaboration skills, with the ability to present findings to senior stakeholders and work effectively across teams.
  • Experience building and contributing to agentic AI architectures and automation frameworks.

Preferred Qualifications, Capabilities, and Skills

  • Experience in customer segmentation, strategic analytics, or related fields within financial services.
  • Hands-on experience with AWS, Databricks for data management, large-scale data processing, and advanced analytics.
  • Understanding of quality assurance practices, model validation, and the importance of data integrity in production environments.
  • Deep knowledge of machine learning/data science theory, techniques, and tools with experience deploying models at scale.
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