Senior Data Analyst

April 13

Wave Mobile Money

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Wave is building a cashless Africa in Senegal, Cote d'Ivoire, Uganda, Burkina Faso, Gambia & Mali. Find us @www.wave.com

501 - 1000

Description

• Be embedded closely with our operations and product teams: deriving insights from customer, merchant, and agent data, visualizing data, and working with operations leads and product managers to improve user and business outcomes. • Navigate large data sets to identify growth drivers and generate testable hypotheses. • Design, develop, implement, and analyze experiments related to product features and operational processes. • Take ownership of the analytics process - from project scoping and design through communicating findings to stakeholders. • Support building up our data infrastructure together with our engineering team. • You may rotate to other product teams over time.

Requirements

• Bachelor's degree or Master's in a quantitative field such as Statistics, Mathematics, Economics, Applied Psychology, Quantitative Social Science, Computer Science, or a related discipline. • 5+ years experience as a data analyst, data scientist, or similar role. • Strong analytical thinking and experience extracting insights using a statistical approach. • Experience running A/B tests and experiments. • Experience working with data visualization platforms (e.g. Tableau, PowerBI, Looker). • Experience collaborating with and supporting cross-functional teams on data analysis and execution of experiments. • Fluent in English, French is a plus. Technical skills: • Strong SQL skills with expertise in querying, manipulating, and analyzing data. • Proficiency in statistical analysis, data cleaning and preparation and data manipulation in Python. • Familiarity with data visualization libraries. Bonus points if you have: • Advanced knowledge of A/B testing and experimental design including sampling, randomization, stratification, and clustering. • Advanced knowledge of statistical methods for analyzing experiments (such as regression modeling and heterogeneous treatment effects analysis). • Advanced knowledge of statistical methods for causal inference with quasi-experimental and time series data (such as propensity score matching, nearest neighbor matching, difference-in-differences, instrumental variable analyses, and structural equation modeling, and other time series methods). • Experience in building machine learning infrastructure and developing/maintaining models in production. • Experience with statistical methods for network analysis. • Experience leading a team of data analysts and/or scientists. • You might be a good fit if you: • Are proficient in SQL and Python. • Are a self-starter that excels at exploring problems and collaborating closely with operations teams to drive growth through data. • Have excellent focus, prioritizing your research and work using an iterative approach (you know when a project is good enough to stop, and you rarely get lost in details). • Are able to compellingly present your findings to

Benefits

• Subsidized health insurance for you and your dependents and retirement contributions (both vary from country to country). • 6 months of fully paid parental leave and subsidized fertility assistance. • Flexible vacation, with most folks taking between 30-40 days per year. • $10,000 annual charitable donation matching.