Principal Data Scientist, Financial Services (Credit Card)

Posted yesterday

analytic recruitingNew York (NY)

Data Scientists Consumer Lending

SENIORITY

Senior

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About the role

Principal Data Scientist, Quantitative Intelligence A leading transaction data intelligence firm powering spend analytics, marketing measurement, and predictive modeling for Fortune 500 clients is hiring a Principal Data Scientist to build the statistical and predictive intelligence that powers their products. You’ll ship end-to-end ML systems, develop rigorous statistical methodologies, and build predictive models on large scale consumer transaction data. This role is hands on, deeply technical, and central to how we transform cleaned and resolved spend data into high value insights.

What you’ll do:

Ship end-to-end ML solutions: research to modeling to production
Build statistical frameworks (balancing, normalization, paneling, cohorting)
Develop predictive models for spend forecasting, propensity, churn, and behavioral embeddings
Create causal measurement systems (synthetic controls, uplift, incrementality)
Apply privacy preserving ML (DP, cleanrooms, aggregation thresholds)
Drive production pipelines and governed model outputs
Mentor senior/staff scientists and represent methodology to stakeholders
What you bring 10+ years in ML/statistics with deep hands-on leadership for financial services
Predictive modeling in financial services / credit card data - spend
Credit card or financial transaction data experience
Strong foundation in weighting, calibration, bias correction
Expertise in supervised learning, forecasting, behavioral/tabular data
Causal inference experience (synthetic control, uplift, incrementality)
Production engineering skills (Python, SQL, cloud data warehouses)
Experience with large scale ML pipelines and evaluation frameworks
Excellent communication and methodological rigor
Representation learning / embeddings
Privacy preserving ML and cleanroom workflows
Keywords: ML Scientist, Statistical Modeling, Predictive Modeling, Paneling, Normalization, Cohorting, Causal Inference, Synthetic Control, Uplift Modeling, Transaction Data, Behavioral Modeling, Forecasting, Representation Learning, Privacy Preserving ML, Cleanrooms, Production ML Pipelines

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