Data Scientist (Growth Team)

Our mission

We're making Africa the first cashless continent.

In 2017, over half the population in Sub-Saharan Africa had no bank account. That's for good reason—the fees are too high, the closest branch can be miles away, and nobody takes cards. Without access to financial institutions, people are forced to keep their savings under the mattress. Small business owners rely on lenders who charge extortionate rates. Parents spend hours waiting in line to pay school fees in cash.

We're solving this by building financial services that just work: no account fees, instantly available, and accepted everywhere. In places where electricity, water and roads don't always work, you can still send money with Wave. In 2017, we launched a mobile app in Senegal for cash deposit, withdrawal, and peer-to-peer and business payments. Now, we have millions of users across six countries and are growing fast.

Our goal is to make Africa the first cashless continent and that's where you come in.....

How you'll help us achieve it

Wave is now the largest financial institution in Senegal, with over 7 million users. And, we’re still in the early days of our product roadmap and potential impact on people’s everyday lives.

We’re looking for an expert in statistical analysis focused on Experimentation and Causal Inference to join the Growth team at Wave. In this role, you will design, run, and analyze experiments in collaboration with product and operations teams to improve the product and customer experience. The aim is to apply rigorous statistical methods of causal inference and machine learning to grow our user base and optimize product features.

In this role you will

• Be embedded closely with our operations and product teams.

• Generate insights using customer, merchant and agent data, which will include running power analyses, statistical modeling, and data visualization.

• Design, implement and analyze experiments to evaluate the causal impact of new product features, promos, and operational process changes.

• Design causal inference analysis plans using longitudinal data.

• Explore large data sets to identify growth drivers and generate testable hypotheses.

• Build and apply machine learning models to improve product features and to analyze heterogeneous treatment effects (e.g. using random forests and related methods).

• Take ownership of the analytics process - from project scoping and design through communicating findings to stakeholders.

Key Details:

• You can work remotely from anywhere (between UTC -5 and +2) with reliable internet access.
• You’re willing to travel to one of our key markets once per year for ~6 days (Wave covers all costs). We also provide a yearly stipend of $800 to meet with coworkers.
• Our salaries are competitive and are calculated using a transparent formula. For this role, depending on your level, we offer a competitive base salary, plus a generous equity package.
• We run performance reviews twice a year and award bonuses or promotions to strong performers who have been with the company for more than six months.
• Major 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.

Requirements

• Education and Experience
• Minimum Bachelor's degree in a quantitative field such as Statistics, Mathematics, Economics, Quantitative Social Science, Applied Psychology, or a related discipline. A Master's or PhD is a plus.  
• 5+ years experience in applied data analytics or similar experience.
• Demonstrated experience running product A/B tests, field experiments, and marketing experiments and conducting causal inference analyses.
• Experience collaborating with and supporting cross-functional teams on data analysis and execution of experiments.
• Experimentation and Causal Inference:
• Deep knowledge of statistics and econometrics.
• Expertise in experimental design including sampling, randomisation, stratification, and clustered designs.
• Knowledge of non-experimental causal inference methodologies, such as propensity score matching, difference-in-differences analysis, and instrumental variable analysis.
• Technical skills
• Strong SQL skills with expertise in querying, manipulating and analyzing data.
• Strong Python skills with expertise in data cleaning, manipulation, and statistical analysis (st