Data Scientist Senior Associate — Business Observability & AI Platform

Job description

Join a fast-moving technology team building a business observability platform for the Payments business—an analytics foundation that enables AI-assisted decision-making for senior leaders and real-time monitoring and alerting for operations teams. You’ll help deliver intraday transaction insights, anomaly detection, forecasting, and conversational analytics supported by a governed metrics foundation. As a Data Scientist Senior Associate at JPMorganChase within Business Observability & AI Platform in Payments, you will own the analytical integrity of what we ship. You will validate the data behind key metrics, define what “correct” looks like for models, and ensure anomaly detection and forecasting outputs remain trustworthy in production. You will partner closely with product, engineering, and design to shape both the roadmap and how we communicate value to stakeholders. Job responsibilities Perform exploratory and diagnostic data analysis across large-scale transactional datasets to validate business logic, metric definitions, and data qualityDefine and document acceptance criteria for analytical features, metrics, and model behavior; verify releases against them before sign-offDesign and run validation and back-testing for anomaly detection and forecasting models, including accuracy, precision/recall, false-positive rates, and threshold calibrationBuild and operate model and data drift monitoring, and recommend retraining or recalibration when performance degradesInvestigate and triage analytical defects, reconciliation breaks, and unexpected model output with upstream data and platform teamsCreate clear data visualizations and analytical narratives for executive and operational audiences, and contribute to product demos and stakeholder materialsPartner with the Technical Product Owner on requirements, user story refinement, and prioritization grounded in evidence from the data Required qualifications, capabilities and skills Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Economics, Engineering, or similar)2+ years of applied data analysis or data science experience, or an advanced degree with relevant internship/project workStrong SQL, including analytical functions and performance-aware querying against large tablesProficiency in Python and the standard analytics stack (pandas, NumPy, scikit-learn, statsmodels or equivalent)Working knowledge of time series analysis, forecasting methods, and anomaly detection techniquesPractical experience with data quality testing, validation frameworks, and reconciliationAbility to build clear, decision-ready visualizations and communicate findings to non-technical stakeholdersComfortable working in an agile, high-ownership environment with shifting priorities Preferred qualifications, capabilities and skills Experience with payments, transaction banking, or financial services dataFamiliarity with semantic layers, metrics stores, or dimensional modelingExposure to columnar or OLAP databases (ClickHouse, Druid, BigQuery, Snowflake)Experience defining model evaluation and drift monitoring in productionFamiliarity with LLM-assisted analytics, evaluation of generated explanations, or prompt-based workflowsVisualization tooling beyond notebooks (D3, Plotly, Tableau, or similar) #LI-RB3