Data Science Intern
Data Science Intern
Affinity Solutions (Affinity) is the leading consumer purchase insights company. We provide a complete view of U.S. and U.K. 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 www.affinitysolutions.com to discover how we’re shaping the future of consumer purchase insights.
About Your Role:
Affinity Solutions seeks a smart, curious, and technically savvy intern to join our cutting-edge Data Science team. The Data Science team builds statistical and machine learning models that clean, normalize, and transform credit card transaction data, and develops panels, cohorts, balancing, and calibration methodologies that power all our ad-tech/mar-tech products at scale by helping customers precisely measure marketing campaign effects, identify consumer spend behavior insights, and inform competitive strategy. In this role, you will focus on advancing Affinity's campaign measurement methodology, with emphasis on improving the quality of synthetic control group creation, enhancing statistical experimentation methods, and tackling related measurement challenges such as data balancing, outlier treatment, and improving sensitivity with small sample sizes.
This role will follow a hybrid structure, working remotely (within the USA) and going into our NY (Manhattan), CA (San Jose), or TX (Plano) office, if applicable.
Duration of Internship Role: Summer 2026 (June through August)
Salary: $25/hr for undergraduate students, $30/hr for current graduate students.
Location: NY (Manhattan), CA (San Jose), or TX (Plano). Must be located in the US for the entirety of the internship.
Your Contributions:
- Engage in R&D to research, evaluate, and prototype new statistical modeling approaches for campaign measurement, including synthetic control group creation, data balancing, and projection methodologies
- Develop and apply metrics and quality measurement frameworks to evaluate and compare the effectiveness of measurement methodology improvements
- Mine large consumer purchase datasets in a cloud environment to support statistical analysis and methodology development
- Conduct literature reviews and explore academic and industry best practices in areas such as pseudo-randomized experiments, causal inference, and observational study design
- Communicate methodologies, findings, and recommendations to management and non-technical stakeholders through written reports and presentations
Your Qualifications:
- Currently pursuing an advanced degree (Master's or PhD) in Statistics, Operations Research, Mathematics, Economics, Computer Science, or other fields that provide rigorous training in statistical modeling and data analysis
- Deep coursework or academic project experience in the fundamentals of statistical reasoning, experimentation, and data modeling
- Familiarity with core concepts in statistical testing, causal inference, experimental and quasi-experimental design, or related areas
- Strong programming skills in Python and SQL, with the ability to analyze large datasets
Preferred Qualifications:
- Knowledge of experimental design or A/B testing concepts and their practical challenges
- Experience working with cloud data warehouses such as Amazon Redshift or Snowflake, and data lakes such as Amazon S3
- Experience writing clean, well-documented, and reproducible code
- Exposure to or interest in advertising measurement, marketing analytics, or the retail/consumer purchase data domain
- Experience with data visualization tools and libraries for communicating analytical results