Moniepoint Inc. is a leading financial technology company that provides a seamless platform for businesses to accept digital payments, access credit and access simple business management tools that enable them to grow with ease. We are the parent company of TeamApt Ltd and Moniepoint MFB and we support over 600,000 businesses to process $12 billion monthly through our digital payment acceptance channels.
Your Opportunity and Mission
- Work closely with product managers, business, development and data engineering teams to guide the marketing and growth team including Growth Product in problem discoveries, monitor and give visibility of product metrics and KPIs.
- Translate product and business questions into analysis and actionable insights.
- Develop and maintain scalable and self-service visualization tools and dashboards.
- Own end-to-end everything analytics related to Marketing and Growth Product: products development cycle, from data requirement gathering and opportunity sizing, to experimentation and post-launch impact analyses.
- Build robust relationships with stakeholders acting as a strategic and supportive link between the technology, business, and product.
- Apply various advanced mathematical and statistical modeling techniques and analyses (A/B test, multivariate test, clustering, supervised learning, etc) to solve for product and business problems that drive the highest impact.
You Have
- 3-4 years of professional experience outside of an academic and internship setting, in a quantitative analysis role in top companies/industries (tier 1 consulting firms, investment banking, tech companies).
- Excellent SQL skills
- Demonstrated experience in designing and analyzing experiments in digital products (A/B test, multivariate, etc.)
- Knowledge of visualization tools like Tableau and Looker and others
- Experience with the application of statistical modeling and advanced analytics to provide product-shaping insights
- Experience in statistical programming (Python) and experience working with popular tools such as Pandas, SciPy, Jupyter/iPython notebooks
- Excellent spoken and written English
Method of Application
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