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Data Scientist

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EMP:Technology
United Kingdom
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Descripción

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 9 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 We're looking for an experienced Data Scientist to join our team.

You'll work on projects across the business, from building credit scoring models and improving fraud detection to optimizing field visit schedules and designing A/B tests for targeted user promotions.

In this role, you'll partner with product managers and business leaders to understand the problems they're trying to solve and find practical ways to use data to improve outcomes.

Our ideal candidate is product-minded, scrappy and thinks.

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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.

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