New opportunity

Applied AI ML Sr Associate

JPMorganChase · Bengaluru, India

About this role

The Data Science and Capability team is a Center of Excellence within CCB Card Modeling focused on ensuring that data used in credit card models and strategies is of superior quality. In this role, the Associate is responsible for attribute quality, developing critical data capabilities using traditional and advanced AI/ML techniques, and deriving new attributes to support revenue growth and risk mitigation across the customer lifecycle.

Responsibilities

Design and develop an attribute library to drive consistency and enhance data quality for machine learning models across customer acquisition, account management, transaction authorization, and collection.
Work closely with senior management to apply cutting-edge machine learning approaches in developing innovative attributes and modeling solutions, and deliver them into production.
Manage attribute quality testing and monitoring using modern techniques to detect data/attribute issues and patterns, enhance consistency, and establish early warning tools.
Evaluate the effectiveness and accuracy of new data gathering techniques and external data sources.
Collaborate across the entire attributing and modeling lifecycle with partners in marketing, risk, technology, data governance, and control.
Own the quality of attributes and models developed, ensuring accurate standards and appropriate implementation.
Contribute creative approaches for analytic project design and problem solving to the team's knowledge base.

Required qualifications

Master's or PhD degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
6 years of experience working with large data, developing or managing the development and implementation of attributes and predictive models in the credit card industry.
At least 3 years of professional coding experience and proficiency in tools such as Python, SAS, Spark, Scala, or TensorFlow, alongside big data platforms like Hadoop, HDFS, Teradata, AWS cloud, or Hive.
Solid understanding of advanced statistical methods and machine learning techniques including GLM/regression, random forest, boosting, tree models, neural networks, clustering, KNN, anomaly detection, simulation, and scenario analysis.
Strong data interpretation, analytical, problem-solving, and communication skills under tight deadlines.

Preferred qualifications

Experience working with US stakeholders.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Machine LearningData SciencePythonSASSparkScalaTensorFlowHadoopHDFSTeradataAWSHiveGLMRegressionRandom ForestBoostingNeural NetworksClusteringKNNAnomaly DetectionSimulationScenario AnalysisPredictive ModelingStatistical AnalysisBig Data

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Data and Attribute Quality Governance for Card ML Models
A core objective is designing and maintaining an attribute library across the customer lifecycle (acquisition, account management, authorization, collections). You must ensure input feature integrity, eliminate data leakage, and establish robust anomaly detection and automated warning systems for card models.
Production-Grade Feature Engineering and Predictive Modeling
The role requires utilizing cutting-edge ML techniques (such as boosting, neural networks, and tree-based models) to derive predictive attributes and deploy models to production. This demands high-quality, scalable code on platforms like Spark, Hadoop, AWS, or Python to drive revenue growth and risk mitigation.
Cross-Functional Collaboration Across the Model Lifecycle
Operating as a Center of Excellence within CCB Card Modeling requires close partnership with marketing, risk, technology, and data governance teams, as well as clear communication of complex analytical results and data strategy to senior management.

Likely challenges to think through

Balancing High-Dimensional Feature Innovation with Credit Risk Governance
Applying advanced ML approaches while adhering to strict credit card regulatory and governance standards requires trading off complex black-box feature representations with auditability and consistency. Prepare by discussing how you establish attribute validation frameworks, monitor data drift, and document feature derivations for governance partners.
Scaling Complex Data Pipelines on Massive Transactional Volumes
Processing large-scale credit card transaction data under tight production deadlines requires balancing algorithmic depth with computational efficiency. Prepare to explain your optimization strategies in PySpark/big data platforms, handling skewed data distributions, and diagnosing pipeline bottlenecks.
Navigating Cross-Time-Zone Stakeholder Alignment
Collaborating effectively with US stakeholders and non-technical business leaders from Bengaluru can lead to communication gaps or misaligned priorities. Prepare concrete examples demonstrating proactive stakeholder management, asynchronous updates, and translating statistical metrics into direct credit risk outcomes.

Practice your answers

Technical practice

How would you design and implement a centralized attribute library for credit card machine learning models?

What to coverSuggested practice topic. Candidates should address data consistency, feature versioning, lineage tracking, and preventing temporal data leakage across customer lifecycle stages (acquisition, account management, authorization, collections).

What statistical techniques and early-warning mechanisms would you use to monitor attribute quality and drift?

What to coverSuggested practice topic. Candidates should discuss metrics such as Population Stability Index (PSI), characteristic analysis, Kullback-Leibler divergence, and automated threshold alerts to detect upstream data corruption or shift before models score in production.

When developing credit risk predictive models, how do you evaluate the trade-offs between GLMs and tree-based boosting algorithms?

What to coverSuggested practice topic. The response should contrast logistic regression/GLM interpretability and regulatory adherence against the predictive power, non-linear capture, and governance challenges of gradient boosted trees (e.g., LightGBM/XGBoost).

How would you design an anomaly detection framework to catch transaction authorization fraud or data pipeline failures?

What to coverSuggested practice topic. Address unsupervised vs. semi-supervised methods such as Isolation Forests, autoencoders, or KNN-based distance metrics, while explaining how to handle high throughput and extreme class imbalance in real-time scoring.

How do you optimize large-scale feature extraction in PySpark or Hive when dealing with massive transactional card datasets?

What to coverSuggested practice topic. Candidates should explain partition pruning, handling skewed keys (e.g., high-activity card accounts), broadcasting lookup tables, and managing memory/spill issues to meet tight execution deadlines.

How do you evaluate whether a new third-party data source genuinely adds incremental predictive power to existing card models?

What to coverSuggested practice topic. Focus on data hygiene assessment, calculating information value (IV) or feature importance alongside baseline models, measuring marginal lift, and considering data acquisition costs and latency.

How would you structure a scenario analysis or simulation to stress-test card attributes against macroeconomic shifts?

What to coverSuggested practice topic. Explain how to apply correlated stress shocks to underlying borrower attributes, simulate severe delinquency distributions under downturn scenarios, and evaluate model robustness beyond historical training distributions.

In neural network architectures applied to card modeling, how do you address overfitting and interpretability for credit decisions?

What to coverSuggested practice topic. Candidates should discuss regularization techniques (dropout, weight decay), loss weighting for imbalanced defaults, and explainability frameworks such as Integrated Gradients or SHAP values to satisfy governance standards.

Behavioural practice

Can you describe a situation where you discovered a significant data quality issue in production models and how you resolved it?

What to coverSuggested practice topic. Use the STAR method to describe identifying corrupted features or pipeline bugs, diagnosing the root cause across data teams, mitigating risk to active models, and communicating the impact to management.

Tell me about a time you had to persuade senior management or non-technical business partners to adopt a complex ML approach.

What to coverSuggested practice topic. Share a specific example demonstrating how you translated intricate model mechanics (e.g., boosting or neural nets) into quantifiable business outcomes, such as reduced credit loss or increased revenue.

Describe an experience where you collaborated across disparate functional teams (such as risk, technology, and data governance) under a tight deadline.

What to coverSuggested practice topic. Outline how you aligned conflicting priorities, maintained rigorous development standards, and ensured smooth production deployment despite cross-functional friction or time constraints.

How have you managed cross-border collaboration and stakeholder alignment when working with geographically distributed teams, such as US partners?

What to coverSuggested practice topic. Provide a concrete example detailing communication strategies, asynchronous alignment, cultural adaptability, and proactive status sharing to keep distributed stakeholders synchronized.

Sharpen your knowledge

Topics to prepare

Credit Risk Modeling and Customer Lifecycle Analytics
Relevance: The job explicitly requires credit card industry experience and focuses on models across acquisition, account management, authorization, and collections. Exercise: Map out standard risk metrics (e.g., probability of default, loss given default) and trace how feature attributes differ when predicting default at acquisition versus monitoring sudden authorization anomalies.
Advanced Statistical Methods and Predictive Machine Learning
Relevance: Explicit requirement for solid understanding of GLM/Regression, Random Forest, Boosting, Trees, Neural Networks, Clustering, and KNN. Exercise: Compare the assumptions and trade-offs of Generalized Linear Models (GLMs) against tree-based boosting algorithms for credit scoring, emphasizing interpretability, regularization, and model governance considerations.
Data Attribute Quality, Anomaly Detection, and Warning Systems
Relevance: Core duty involves designing attribute libraries, managing attribute quality testing/monitoring, identifying patterns/issues, and establishing early warning tools. Exercise: Formulate a statistical framework (e.g., Population Stability Index, drift detection, distribution divergence, and outlier identification) to detect corrupted or shifting feature attributes before they hit production ML models.
Scenario Analysis and Simulation Techniques
Relevance: Specifically listed in the required qualifications alongside advanced statistical methods for risk mitigation and strategic decisions. Exercise: Walk through a stress-testing scenario where macroeconomic shocks impact credit card loss distributions, demonstrating how to simulate correlated attribute shocks across an active card portfolio.

Skills to revise

Large-Scale Data Engineering and Manipulation in PySpark / Hive
Relevance: Explicit requirement for proficiency in big data platforms (Spark, Hadoop, HDFS, Hive, Teradata) to manipulate and summarize massive datasets. Exercise: Write a PySpark script that computes rolling 30/60/90-day transaction aggregations and velocity attributes per credit card account, optimizing partitions to prevent data skew.
Predictive Modeling and Attribute Construction in Python / SAS
Relevance: Stated requirement for at least three years of professional coding proficiency in tools such as Python or SAS, including tree-based models and regressions. Exercise: Implement a gradient-boosted classifier using Python (scikit-learn/LightGBM) on tabular credit data, including cross-validation, hyperparameter tuning, and calculating feature importance metrics.
Deep Learning / Neural Networks Implementation via TensorFlow
Relevance: TensorFlow and neural networks are explicitly listed among acceptable modeling platforms and machine learning techniques. Exercise: Build a baseline sequential neural network in TensorFlow to classify anomalous transactional behavior, applying appropriate dropout and loss weighting for severe class imbalance.

Your preparation checklist

Prepare an end-to-end credit risk modeling and feature engineering project walkthrough
Construct a concise, structured presentation of a past credit card project (covering acquisition, authorization, account management, or collections). Clearly detail how raw transaction data was transformed into predictive attributes, the statistical/ML models used (e.g., GLM vs. boosting), deployment to production, and measurable risk or revenue impact.
Design a conceptual framework for an attribute library and data monitoring system
Outline an architecture for a centralized attribute library that enforces consistency and automated quality checks. Document concrete mechanisms for tracking population stability index (PSI), characteristic drift, missingness spikes, and early-warning alerts before attributes flow into production credit models.
Review big data and distributed computing optimizations in PySpark/SQL
Prepare code snippets and spoken explanations demonstrating proficiency in big data platforms (Spark, Hive, Hadoop). Focus on solving real-world performance bottlenecks such as skewed transaction keys, rolling window aggregations, broadcast joins, and memory management under tight processing SLAs.
Synthesize examples of cross-functional governance and partner collaboration
Map out concrete STAR-method examples demonstrating alignment with risk, compliance, data governance, and technology teams. Highlight how you ensure strict development and implementation standards while addressing conflicting stakeholder priorities and regulatory requirements.
Draft evidence of executive communication and distributed stakeholder management
Formulate 2-3 concise case summaries illustrating how you presented technical ML trade-offs and statistical insights to senior leadership, including specific communication strategies used when collaborating with overseas or US-based stakeholders.
Formulate role-specific technical and strategic questions for the interviewers
Draft thoughtful questions addressing the current maturity of CCB's attribute library, prevailing infrastructure standards (e.g., transition between on-prem Hadoop/Teradata and AWS cloud), and how model governance balances cutting-edge AI experimentation with auditability.

Availability has not yet been verified. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…