Senior / Principal Data Scientist – Causal Inference & Forecasting on Credit Card Spend DataTwo openings: Senior and Principal · Hands-on individual contributor roles · Remote (US)WHY THIS ROLEOur client, a consumer data and analytics company serving brands and agencies, turns large-scale credit card transaction data into insight they act on. Its quantitative data science team owns the hardest statistical questions in that data: what actually caused a change in spending, how to make an imperfect panel represent the real population, and what consumers will spend next. These are not off-the-shelf problems. You will design custom methods, ship them to production, and defend them to sharp, technical stakeholders. If you want to stay deeply hands-on in modeling rather than move into people management, this is built for you. WHAT YOU WILL WORK ONCausal measurement: Measure whether marketing campaigns actually cause additional consumer spending, using synthetic controls, uplift modeling, incrementality testing and experiment design. Panel weighting and bias correction: Make a transaction panel representative of the population through weighting, calibration and sample-bias correction, at a sensible cost. Predictive modeling and forecasting: Forecast consumer spend and model propensity, churn and behavior, including behavioral embeddings built from transaction histories. Privacy-preserving analytics: Work within clean rooms, differential privacy and aggregation thresholds without losing statistical rigor. Production ML: Take models from prototype to governed, monitored pipelines in Python, SQL and a cloud data warehouse.
WHAT WE ARE LOOKING FOR:
Hands-on experience with credit card transaction data (required). You have built models on card spend data, not only analyzed it. Direct, hands-on depth in at least one of three focus areas: causal inference/experimentation, sample weighting/bias correction, or predictive modeling/forecasting of consumer behavior. Adjacent work does not substitute. A track record of solving complex, non-trivial problems with methods you designed or substantially adapted, not just applied. Strong statistics and machine learning on large tabular or behavioral data. Production-grade Python and SQL; experience with cloud data warehouses and large-scale ML pipelines. Clear, concise communication: you can explain and defend a methodology to a technical audience in a few sentences. Senior level: a proven hands-on record in one or more focus areas; you own projects end to end. Principal level: 10+ years in ML/statistics; you also set methodology across the team and mentor senior scientists, while staying hands-on. PROBABLY NOT THE RIGHT FIT IFYour credit card data experience is mainly in credit risk, fraud, AML, or loss/reserve forecasting. These are valuable skills, but the problems and tech stack differ from this team's. You have mainly moved into managing teams and are no longer building models yourself. 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
