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Data Scientist

Wave Mobile Money

Trabajo remoto

Ubicación
Remote (Burkina Faso, Ghana, Kenya, Senegal, Spain, United Kingdom, United States)
Retribución
USD 130600-236200 per annual
Jornada
Full Time
Publicado
14 oct 2026

Confirma que la oferta sigue abierta y revisa sus condiciones en el sitio web de la empresa antes de solicitar el puesto.

Descripción del puesto

La interfaz está en español. Los títulos y las descripciones de las empresas se mantienen en el idioma original de publicación, que puede ser inglés.

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 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 This is a fully remote role. Candidates must be based in one of our talent hub countries (US, UK, Spain, Kenya and Ghana) or in one of our operating markets in Africa including Senegal, Côte d'Ivoire, or Burkina Faso. Wave provides a yearly $1,200 stipend to support coworking meetups with teammates. Remote team members are expected to travel to our operational markets (e.g. Senegal or Côte d'Ivoire) at least once a year. Exceptions apply, but we’ve found this key to understanding our users and product. Our salaries are competitive and are calculated using a transparent formula. For this role, depending on your level and location, we offer a salary between $130,600 - $236,200 USD, plus a generous equity package. 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. 5+ years of experience in data science, machine learning or technical analytics roles. Technical Skills Deep expertise with Python, SQL, and data visualization tools Experience in one or more of the following: machine learning, causal inference, statistical modeling, or A/B testing. Experience applying these methods in operational settings with offline components (example: incentive structures for field staff) is a plus. Highly effective written communication and presentation skills A track record of translating complex data into clear, actionable insights for both technical and business stakeholder Fluency in English. French is a plus. You might be a good fit if you Have a bias for action and ability to thrive in ambiguous, fast-moving environments where you must create clarity and drive forward progress. Have worked in early stage product teams (or a rapidly scaling product) and are able to drive product decisions. 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, Uganda, Niger, Sierra Leone, and Cameroon 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. We are on Y Combinator's top 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. Originally posted on Himalayas

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Elige ejemplos reales de tu experiencia que respondan a los requisitos del puesto. Explica tu aportación y utiliza cifras solo cuando puedas justificarlas. Sigue las instrucciones de la empresa y revisa los datos de contacto, el contenido y el PDF antes de enviar la candidatura.

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Preguntas sobre la búsqueda de empleo

¿Por qué algunas ofertas están en inglés?

Las empresas publican sus propios títulos y descripciones. Conservamos ese texto para no alterar requisitos o condiciones. La navegación y esta guía están en español. Si una búsqueda en español no encuentra ofertas, prueba también el título o la competencia en el idioma del anuncio, por ejemplo «software engineer».

¿Un puesto remoto permite trabajar desde cualquier país?

No necesariamente. La empresa puede limitar los países de residencia, exigir autorización para trabajar o fijar un horario. Comprueba estos puntos en la oferta original. Si no aparecen, consúltalos con la empresa antes de asumir que puedes trabajar desde tu ubicación.

¿Qué hago si no aparecen resultados?

Prueba un título más general o una sola competencia y elimina los filtros de uno en uno. Los nombres de los puestos varían entre empresas. Si una oferta concreta ha desaparecido, busca su referencia en la página de empleo de la empresa; ampliar los filtros no recupera una vacante cerrada.

¿La candidatura se envía desde ResumizeAI?

El botón de solicitud abre un destino externo. Sigue las instrucciones de la empresa o del proveedor de selección y confirma allí el envío. Preparar un currículum en ResumizeAI no equivale a presentar una candidatura. Si el enlace solo abre la web de la empresa, busca la vacante antes de continuar.

¿Cómo adapto el currículum y la carta de presentación?

Relaciona los requisitos con proyectos, tareas y resultados que puedas explicar. Destaca lo relevante sin inventar experiencia ni copiar competencias que no tienes. En la carta, explica tu interés y aporta un ejemplo concreto. Respeta el idioma y el formato pedidos y revisa ambos documentos antes de enviarlos.