Sr Data Analyst

MoneyGram•Dallas, TX

21h•Remote

About The Position

We are seeking a versatile data analytics team member to support our growing data and analytics initiatives in the areas of Finance, Product, and Retail. In this role, you will support the MoneyGram organization and drive rapid, data-driven insights to help drive key business decisions. This is a full-time remote position open to candidates based in the US or Canada, offering the opportunity to work globally and influence product strategy across multiple areas of the product and technology offerings. MoneyGram is in a period of rapid transformation through its re-founding. Examples of companies who successfully re-founded themselves are Microsoft, Apple, and Uber. We are looking for re-founders who can step into MoneyGram and help drive a fundamental reinvention of our strategy, culture, and ways of working while staying true to our mission. MoneyGram is a PE-backed private company building towards a near-term IPO. What you will get to do as Senior, Data Analyst: · Analytics Re-Invention: AI has become the fundamental building block of many new platforms built in the last few years. You are willing to go beyond anything you’ve ever done before and rethink analytics from the ground up. “it’s been done this way before” is not a benefit, but a defect. · Customer Experience and Product Performance: Craft and evolve the view of the customer experience through analytics and product metrics. Drive a deep understanding of the impact of product experimentation and development on customers and partners. · Insights & Reporting: Analyze partner, customer, and enterprise data to extract actionable insights and identify trends. Develop key visual representations of data related to company and/or product performance. Communicate findings clearly to team members, translating data into strategic recommendations for both technical and non-technical stakeholders. · Cross-Functional Collaboration: Collaborate closely with cross-functional teams – from Data, Analytics, Product, Sales, Marketing, Finance, and Regional/International teams – to represent and drive insights to business goals. · Technology & Automation: Champion for best-in-class analytics tools and processes. Work with Engineering, Data Engineering, and Analytics teams to implement and leverage analytics platforms (BI tools, tag management, automation via APIs) that enhance company insights. Stay abreast of industry trends and emerging technologies in product analytics, continuously seeking opportunities to improve our capabilities and methodologies · Experimentation & Optimization: Provide the stakeholder with the ability to measure the success of feature or functionality roll-out by pulling and organizing the data into cohorts and measuring metrics based on cohort performance.

Requirements

Technical Skills: Strong hands-on skills in data analysis. Proficiency in SQL for querying and manipulating large datasets. Familiarity with analytics programming (Python and/or R for statistical analysis) is desirable.
Data Vizualization Skills: Strong Experience with business intelligence and data visualization tools, Looker preferred, or similar BI platforms.
Data-Driven Mindset: Analytical thinker with a detailed understanding of statistical analysis and experiment design (e.g. hypothesis testing, A/B and multivariate experiments, regression analysis). Able to quickly uncover insights from data and use them to drive actionable recommendations and measurable results.
Communication & Influence: Excellent communication and interpersonal skills. Ability to distill technical complex data findings into clear, compelling narratives for both technical and non-technical audiences. Skilled at visual representation of insights, documenting, and presenting with explanation to stakeholders.
Cross-Functional Collaboration: Comfortable working cross-functionally and with stakeholders – you can build strong relationships with stakeholders, team members, and technical partners and have experience advising or partnering with various backgrounds across the organization.
High-Growth & Global Experience: Thrives in a fast-paced, high-growth and ambiguous environment. Global experience and the ability to shape insights for global audience is highly valued.
Industry Background: Experience in direct-to-consumer businesses is nice to have; fintech or financial services experience is a strong plus.
Education: Bachelor’s or Master’s degree in a quantitative field (Statistics, Economics, Mathematics, Computer Science, etc.) or equivalent practical experience. An advanced degree or relevant industry certifications in analytics/data science is a bonus.

Nice To Haves

A passion for evolving products and using data to enhance customer experience is essential.
An advanced degree or relevant industry certifications in analytics/data science is a bonus.

Responsibilities

Analytics Re-Invention: AI has become the fundamental building block of many new platforms built in the last few years. You are willing to go beyond anything you’ve ever done before and rethink analytics from the ground up. “it’s been done this way before” is not a benefit, but a defect.
Customer Experience and Product Performance: Craft and evolve the view of the customer experience through analytics and product metrics. Drive a deep understanding of the impact of product experimentation and development on customers and partners.
Insights & Reporting: Analyze partner, customer, and enterprise data to extract actionable insights and identify trends. Develop key visual representations of data related to company and/or product performance. Communicate findings clearly to team members, translating data into strategic recommendations for both technical and non-technical stakeholders.
Cross-Functional Collaboration: Collaborate closely with cross-functional teams – from Data, Analytics, Product, Sales, Marketing, Finance, and Regional/International teams – to represent and drive insights to business goals.
Technology & Automation: Champion for best-in-class analytics tools and processes. Work with Engineering, Data Engineering, and Analytics teams to implement and leverage analytics platforms (BI tools, tag management, automation via APIs) that enhance company insights. Stay abreast of industry trends and emerging technologies in product analytics, continuously seeking opportunities to improve our capabilities and methodologies
Experimentation & Optimization: Provide the stakeholder with the ability to measure the success of feature or functionality roll-out by pulling and organizing the data into cohorts and measuring metrics based on cohort performance.