Data Analyst – Merchant Solutions

Aperia•Alpharetta, GA

1d•Onsite

About The Position

Join Aperia Solutions, a leader in SaaS solutions for the Payments and Compliance industries. Aperia is a Texas-based fintech and managed consultancy firm that creates custom SaaS applications and other software-based solutions for the payments, banking, and processing industry. Founded in 1999, Aperia offers business intelligence, risk management, compliance, and customer intelligence platforms. With offices in Dallas, Washington DC, and Vietnam, Aperia is a fast-paced, global organization that strives to improve efficiency in compliance, risk, and customer service operations. Aperia’s clients include banks, processors, payment facilitators, merchant service providers, independent sales organizations, and government entities. A career at Aperia promises a great challenge, culture, and opportunities to forge your own path. About the Role: We are seeking a highly analytical Data Analyst to support our SMB Retention & Engagement efforts. This role will be part of the SMB Business Transformation organization and will own the data, reporting, segmentation, and statistical measurement needed to execute our retention and engagement roadmap across digital and traditional outreach/engagement channels. You will be responsible for building the analytical foundation that drives merchant targeting, offer adoption, channel optimization, and program scaling. This is a high‑impact role supporting cross-functional partners in Product, Customer Support/Ops, Sales, and Data Science.

Requirements

Bachelor’s degree in business, finance, analytics, engineering, or a related field—or equivalent experience
3+ years of experience in data analytics, business intelligence, or statistical analysis
Strong SQL skills and experience working with large, complex, siloed datasets
Proficiency with BI tools (Looker, Tableau, Power BI, or similar).
Solid grounding in statistics, including: experimental design (A/B tests, randomized trials), hypothesis testing and statistical significance, regression modeling and causal inference, confidence intervals and variance estimation
Ability to present complex data in a clear, concise, and actionable manner
Demonstrated ability to break down ambiguous problems into structured components
Excellent communication and presentation skills
Experience working in fast-paced, matrixed environments
Must be willing to submit to a background investigation and drug test as part of the selection process.
Applicants must have authorization to work in the country without the need for sponsorship.

Nice To Haves

Master’s degree in Data Science, Statistics, Analytics, Economics, or a related quantitative field
Experience in fintech, payments, SaaS, or lifecycle/CRM programs is a plus, in a role supporting small businesses is a bonus
Experience building predictive models (e.g., churn prediction, propensity scoring, uplift models)
Familiarity with experimentation platforms or statistical libraries (e.g., scikit‑learn, statsmodels, R)
Experience working with marketing automation, CRM, or lifecycle engagement tools

Responsibilities

Own and maintain all datasets supporting SMB retention and engagement initiatives
Develop scalable data models for merchant cohorts, lifecycle stages, churn indicators, and offer eligibility
Build dashboards and reporting for all program workstreams and deliver weekly/monthly reporting for leadership and operational teams
Develop merchant selection, segmentation, eligibility criteria, prioritization rules, and ranking frameworks for all retention and engagement campaigns
Collaborate with cross-functional teams to refine offer strategy through data-driven insights
Design statistically rigorous test vs. control frameworks for campaigns and lifecycle interventions
Apply appropriate analytical methods (e.g., hypothesis testing, regression, uplift modeling, time-series approaches) to evaluate program impact
Partner with cross-functional teams to define success metrics and attribution logic
Conduct deep-dive analyses on churn drivers, behavioral patterns, channel performance, and campaign effectiveness
Translate complex analytical findings into clear, actionable recommendations