Principal Data Scientist, Quantitative Intelligence (Consumer Transaction Data)

Principal Data Scientist, Quantitative Intelligence (Consumer Transaction Data)

Reports to: Head of Data Science

About the role

Our client, a leader in consumer purchase data and analytics, turns large-scale card transaction data into intelligence for brands, investors and agencies. They're hiring a Principal Data Scientist to be the hands-on technical lead for the team that turns this data into statistical methods and machine learning models. This is an individual-contributor tech lead role. You'll guide the work of several data scientists, with a path to people management.

A partner team handles data cleaning, merchant resolution and categorization. This role picks up from there: making the data statistically sound and predictive, then shipping it.

What you'll do
• Own models end to end: design the methodology, build it, deploy it to production and monitor it.
• Build the statistical layer that makes analysis on the data trustworthy: panel weighting, balancing, normalization, cohorting and bias correction.
• Build predictive models from past behavior, such as spend prediction, category and brand trend forecasting, propensity and churn, and behavioral embeddings.
• Apply causal methods (incrementality, synthetic control, uplift) where the problem calls for them.
• Set technical direction, review the team's work, and explain and defend methodology to stakeholders and clients.

Must-haves
• Hands-on experience with credit card or card transaction data.
• A track record of shipping ML and statistical models to production yourself, not just analysis or prototypes. Strong production engineering in Python and SQL on a cloud data warehouse.
• A deep statistics foundation: sampling, weighting and calibration, bias correction, and judging when a result can be trusted.
• Predictive modeling and forecasting built on raw, transaction-level behavioral data.
• [8+] years of experience, including leading the technical work of other data scientists.
• Clear, concise communication of methodology.

Nice to have
• Causal inference and experimentation, as a complement to ML.
• Representation learning and embeddings.
• Privacy-preserving ML and data clean rooms.

This role is not focused on fraud, AML, credit risk or loss/reserve forecasting, and it is not a pure experimentation or A/B testing role.

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