Data Scientist (Machine Learning)

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 experienced Data Scientist with experience in ad hoc data analysis, optimization, and machine learning to join our team. You will work on a wide range of data analysis projects across teams, such as supporting call volume forecasting, optimizing operation field visit schedules to maximize impact, targeted promos for users, improving our fraud models, and more!

This role is unique in a way that you will collaborate with a wide range of product managers and business leaders to understand their problems and use data science to optimize their operations. We are looking for a data scientist who is product-minded, scrappy and thinks about data in terms of intuitive user behaviours rather than just inputs to a black box model.

In this role you’ll;

• Be embedded closely with our product and operations teams.
• Generate insights using customer, merchant and internal data using statistical modelling, data visualization, and machine learning.
• Design, implement and analyze optimizations for our internal operations, user promos, and agent network to help teams scale and reduce costs.
• Build data-driven heuristics and machine-learning models to improve user and business outcomes.
• Take ownership of the analytics process - from project scoping and design to 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. We also offer 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, Computer Science, Engineering, or a related discipline. A Master's or PhD is a plus.
  • 4+ years experience in applied data science or similar experience.
  • Demonstrated experience with product A/B tests, optimization, and exploratory data analysis
  • Experience collaborating with and supporting cross-functional teams on data analysis and execution of experiments.
• Technical Skills
  • Strong knowledge of probability and statistics
  • Strong SQL skills with expertise in querying, manipulating and analyzing data
  • Strong Python skills with expertise in data cleaning, manipulation, and statistical analysis (strong R skills are also acceptable and a willingness to quickly adapt to Python).
  • Proficiency in applying machine learning methods to solve business problems
  • Familiarity with data visualization libraries.
• Fluency in English. French is a plus.

You might be a good fit if you

• Are a self-starter who excels at exploring problems and collaborating closely with product and operations teams.
• Like to ask questions of data and just have to find out the answers.
• Prefer solving data problems using exploratory data analysis and statistics over applying black-box models
• 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 technical and non-technical audiences and make proactive recommendations based on data.

Our team

• We have a rapidly growing in-country team in Senegal, Côte d'Ivoire, Mali, Burkina Faso, The Gambia, and Uganda, plus remote team members spread across the world.
• We're deeply passionate about our mission of bringing radically affordable financial services to the people who need them most.
• We foster autonomy for our employees. You'll own your projects at every stage, from understanding the problem to monitoring your solution in production.
• We raised the largest Series A in Africa in 2021. Our world-class investors, include Founders Fund, Sequoia Heritage, Stripe, Ribbit Capital, Y Combinator, and Partech Africa.
• In 2023, we were on Y Combinator's top 50 companies by revenue.

How to apply

Fill out the form below, and upload a resume in English and a cover letter describing your interest in Wave and the role.

We review applications frequently and recommend that you apply to the role that most closely aligns with your skills, experience and career goals.

Wave is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.