Embark on a fulfilling and challenging career as a Business Analyst II with the Data Intelligence and Solutions team in our Credit Card Rewards organization. You'll have the chance to make a significant impact by supporting process improvements and key initiatives while expanding your creative skills in a supportive and collaborative environment. Join us to contribute to our mission and advance your career.

As a Business Analyst II within JPMorganChase, you will play a crucial role in enhancing operational efficiency and driving strategic initiatives across rewards misuse prevention, customer feedback analysis, and operational reporting. By leveraging your advanced understanding of data analytics and automation, you will sit between data, models, and business users, analyzing complex data sets to uncover patterns and turning analytics and machine learning output into practical, trustworthy products for Operations, Risk, Audit, and senior leaders. Your expertise in cross-functional collaboration will enable you to work effectively with diverse teams, ensuring alignment with organizational goals. You will be responsible for planning and directing work, making decisions that impact departmental outcomes, and managing complex situations where the right analytical approach is not obvious. Your strategic thinking and strong customer service skills will be essential in delivering results that enhance the customer journey and drive business success.

Job responsibilities

Analyze and interpret complex data sets from various sources, utilizing advanced data analytics skills to uncover patterns and provide insightful reporting in support of operational and strategic initiatives, across rewards, redemption, exception, and partner performance data, and build, validate, and interpret detection models on large card datasets using unsupervised methods for pattern discovery and supervised methods as labeled outcomes accumulate.
Develop and implement automation strategies, leveraging systems architecture knowledge to optimize processes and drive departmental efficiency, including the text classification pipelines behind complaint and feedback analysis, their ground truth sets and per-category accuracy, and monitoring of deployed models and rules for drift, recalibration, and retraining.
Coordinate cross-functional collaboration, working effectively with diverse teams across the organization to align efforts, share knowledge, and drive the successful implementation of business strategies, defining which metrics, scores, and explanations reach each user group, shaping the dashboards and summaries that carry them, and presenting results to technical and senior non-technical audiences.
Utilize strategic thinking to evaluate potential scenarios, assess risks, and make informed decisions that directly impact departmental outcomes, translating model output with business partners into decision rules, thresholds, and scoring bands, and recommending the right approach per problem across rules, statistics, and machine learning, weighing accuracy, explainability, and governance.
Provide coaching to team members, empowering them to take ownership of their work while ensuring objectives are met efficiently and effectively, designing how operational decisions and case outcomes are captured as labeled data so each model version improves on the last, and documenting objectives, data sources, methodology, assumptions, and limitations to a standard that withstands independent validation and audit review.

Required qualifications, capabilities, and skills

Demonstrated proficiency in developing and implementing automation strategies, with a strong understanding of systems architecture, including Python for modeling (pandas, NumPy, scikit-learn) and advanced SQL with window functions and query optimization on a cloud platform such as Snowflake, Databricks, BigQuery, Redshift or similar.
3+ years of experience in data science, advanced analytics, or a related quantitative role.
Proven ability to coordinate cross-functional collaboration, with experience in working with diverse teams across an organization, translating technical findings into concise business narratives for senior audiences, and building user-focused reporting in Tableau, Power BI, or Looker.
Advanced strategic thinking skills, with a track record of evaluating potential scenarios, assessing risks, and making informed decisions, including hands-on machine learning across classification, anomaly detection, and clustering, and the judgment to know when rules or simpler statistics beat machine learning, and what each implies for governance.
Experience in providing coaching and technical guidance to team members, with a focus on empowering individuals and ensuring efficient achievement of objectives, supported by sound evaluation practice across model explainability, class imbalance, data leakage, and validation design, and familiarity with version control and code review (Git, Bitbucket, or similar).
Provide quality service to customers through continuous communication, with strong written communication including methodology and process documentation.
Understand software delivery lifecycle and have skills in industry-standard methodologies and related tasks, including hands-on ETL and data preparation workflow experience in an established platform, and handling of personally identifiable or regulated data under defined access controls.

Preferred qualifications, capabilities, and skills

Capability to leverage artificial intelligence and AI tools to enhance data analysis, uncover business trends, and provide actionable insights for strategic decision-making, including large language model APIs, structured output, and evaluating output quality against a defined framework.
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, or a related quantitative field.
Proficiency in implementing automation solutions to streamline business processes and improve operational efficiency, including agentic or tool-calling systems with multi-step workflows, function calling, and their guardrail and evaluation design.
Expertise in applying customer service and conflict management skills to understand client needs, resolve stakeholder issues, and facilitate effective collaboration, with domain experience in fraud, abuse, financial crime, or rewards program integrity, and exposure to model risk management frameworks.
Ability to craft clear and effective prompt writing to guide data analysis and ensure consistent outcomes, including prompt design for classification and extraction tasks.
Ability to contribute to a collaborative work environment by sharing knowledge and supporting team initiatives, including experience designing or analyzing experiments and using data to influence product or process change.
Competence in technology/process release management, with proficiency in using software applications, digital platforms, and other technological tools to solve problems and improve processes, including MLOps practice (experiment tracking and model registry such as MLflow, model versioning and deployment, drift monitoring and retraining), Databricks, Spark or PySpark, orchestration such as Airflow, and volumes requiring partitioning, Parquet, or Amazon S3.