About Your Role The Data Science team at Affinity Solutions builds the statistical and machine learning capabilities that turn raw credit card transactions into an AI-ready source of truth for consumer spending behavior • the models that resolve messy transaction strings into canonical brands and categories, the predictive models that turn spend history into forward-looking signals, and the methodology that measures campaign effects defensibly. Increasingly this work will be consumed by models and agents rather than by analysts, which raises the bar on correctness, robustness and privacy.
In this role, you will serve as the technical lead for the Quantitative Intelligence area, spanning three core disciplines • predictive modeling, statistical weighting and paneling methodology, and campaign measurement. The goal of this group is to turn Affinity's consumer spend data into predictive and statistical intelligence • models and signals that are served as first-class, governed capabilities to our customers, our products, and the AI agents that will increasingly consume our data. Example problems include predicting a customer's future spend at a merchant, long-term brand and category spend forecasting, modeling ticker performance, propensity and churn models, privacy-safe behavioral embeddings, reusable feature and training-set generation for customer-built models, pseudo-randomized campaign measurement, and evaluation methodologies and frameworks that ensure these models work well.
You will work hands-on alongside this team while also setting its technical roadmap, with a path to formally managing this group as it grows.
Your Responsibilities - Set the technical roadmap and standards across predictive modeling, statistical weighting and paneling, and campaign measurement, so the three disciplines share one methodological foundation rather than diverging practices.
- Serve as tech lead for a team of scientists and engineers spanning these three areas.
- Own and advance the paneling and weighting framework that creates cost-optimized panels • stable subsets of data that reduce costs while balancing and normalizing the data to maintain representativeness and statistical quality.
- Contribute to our campaign measurement methodology • synthetic control creation, identity resolution, metric computation • and ensure it meets the reproducibility and audit standards the architecture requires.
- Guide the R&D roadmap for predictive models over the full transaction history • merchant-level spend prediction, category and brand forecasting, propensity and churn scores, and brand/ticker performance models, including model evaluation, feature/label pipelines, and embeddings work that supports them.
- Apply and champion privacy-preserving modeling techniques • aggregation thresholds, perturbation-aware modeling, and differentially private training • across all three disciplines, and ensure models operate correctly within cleanroom constraints.
- Drive production ML and statistical pipelines to run reliably and cost-efficiently at scale, including large-scale batch scoring and weighting computations.
- Mentor senior and staff-level team members and serve as the company's senior-most ML/statistics authority to clients and stakeholders.
- Communicate methodologies and results to management, clients, and other non-technical stakeholders, including defending methodology under external scrutiny.
Your Qualifications - Substantial experience as a technical lead • setting technical direction and roadmap for other data scientists and ML/software engineers, driving cross-team standards, and unblocking others• hardest problems. Prior formal people-management experience is a plus but not required (see Preferred Qualifications).
- Extensive experience with leading production ML projects end-to-end, with emphasis on quality and scalability.
- Deep knowledge of the fundamentals of Machine Learning and Statistics. Proven ability to conceptualize business problems and craft sound and practical ML/statistical solutions.
- Experience with time-series data and forecasting problems.
- Strong experience with supervised learning on large-scale tabular and behavioral data • gradient-boosted trees, regularized regression, and neural approaches • including regression on sparse, zero-inflated, heavy-tailed targets such as consumer spend.
- Experience with panel weighting and bias-correction methodologies • propensity weighting, calibration, projection, or similar • applied to large, imperfectly-matched populations.
- Experience with causal inference and experimental design • incrementality and lift measurement, matched or synthetic control, uplift modeling.
- Proven experience designing model evaluation and backtesting frameworks, with fluency in calibration, ranking and uplift metrics, and drift detection.
- Experience setting up infrastructure to support the ML development lifecycle, with attention to compute and data cost at scale.
- Strong software engineering and data engineering experience. Solid knowledge of Python and SQL. Experience writing production quality code.
- Experience working with cloud data warehouses such as Snowflake, Amazon Redshift, and data lakes such as Amazon S3.
- Skilled at using AI to support and accelerate software development and ML experimentation.
- Entrepreneurial, highly self-motivated, collaborative, keen attention to detail, willingness and capable to learn quickly, and ability to effectively prioritize and execute tasks in a demanding environment.
- Great communication skills (verbal, written and presentation), including proven ability to represent technical methodology to non-technical stakeholders and clients.
- Advanced degree in Statistics/Mathematics/Computer Science/Economics or other fields that provide advanced training in data modeling and analytics, and 10+ years of industry experience.
Preferred Qualifications - Experience working with Financial data, especially card transaction data.
- Experience or exposure to large consumer and/or demographic data sets.
- Experience with representation learning and embeddings for user, sequence, or behavioral data.
- Experience with privacy-preserving machine learning • differential privacy and DP-SGD, k-anonymity and aggregation thresholds, data cleanrooms such as AWS Clean Rooms, Snowflake, BigQuery, or Databricks.
- Experience with semantic or metric layers, feature stores, or otherwise publishing model outputs as governed, versioned data products.
- Exposure to LLMs and AI agents as consumers of model outputs through typed APIs and protocols such as MCP.
- Prior experience formally managing a team of data scientists or ML/software engineers • hiring, performance management, and career development.
Salary Range: $200,000 • $215,000
Office Hours: 9:00 AM to 5:30 PM
Benefits for full-time employees of Affinity Solutions begin on the first of the month following your date of hire with a generous employer contribution for medical, dental, and vision. In addition to company paid holidays, wellness time off, other wellness benefits, and employee discounts, you will also get employer paid life insurance and have the option to enroll into an employer-matched 401K Plan. We strongly encourage work/life balance by providing unlimited vacation days, available starting 90 days from your hire date as a team member.
