Principal Data Scientist (64_2026.3)

Affinity Solutions; United States; Hybrid; USD 200,000 - 215,000; Full time

Benefits offered by this job

Medical, dental & vision

401K plan

Unlimited vacation

Life insurance

Job summary

Affinity Solutions is seeking a Senior Data Scientist to lead the Quantitative Intelligence group, shaping predictive models and measurement methods across the data stack.

You will own the R&D roadmap, mentor staff, and communicate methodologies to clients while ensuring privacy-preserving techniques and scalable solutions. Competitive salary and US-based hours included.

Qualifications

• Substantial experience leading data science and ML teams with cross-team collaboration.
• Proven ability to design scalable ML/statistical solutions for large data.
• Experience with privacy preserving modeling and cleanroom constraints.

Responsibilities

• Set technical roadmap for predictive modeling, weighting, paneling, and campaign measurement.
• Lead scientists and engineers across three focus areas.
• Own and evolve the paneling/weighting framework for representativeness and cost efficiency.
• Drive production ML pipelines and client-facing methodological communication.

Skills

Machine Learning

Statistics

Python

SQL

Leadership

Cloud platforms

Education

Advanced degree in Statistics/Math/CS/Economics

Tools

Snowflake

Redshift

Databricks

AWS

Job description

About Affinity Solutions

Affinity Solutions Affinity is the leading consumer purchase insights company We provide a complete view of US and UK consumer spending across and between brands via exclusive access to fully permissioned data from over 100 million consumers Our proprietary AI technology Comet transforms these purchase signals into actionable insights for business and marketing leaders to drive optimal outcomes and build lasting customer relationships Visit wwwaffinitysolutionscom to discover how were shaping the future of consumer purchase insights

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 Affinitys 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 customers 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 brandticker performance models including model evaluation featurelabel 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 companys senior most MLstatistics 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 MLsoftware 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 MLstatistical 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
• Experience working with Financial data especially card transaction data
• Experience or exposure to large consumer andor 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 MLsoftware engineers hiring performance management and career development

Salary & Hours

• Salary Range 200000 215000
• Office Hours 900 AM to 530 PM

Benefits

• 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 worklife balance by providing unlimited vacation days available starting 90 days from your hire date as a team member.