Our data scientist will be in charge of creating data projects understanding our business and developing mathematical models that can impact our operations and customer experience.

Here you are going to be responsible for:

  • Building models to improve risk management considering money-in and money-out operations

  • Rolling out logic (or algorithms) to software to detect fraudulent patterns

  • Helping the team forecasting and predicting questions of key metrics related to payments approval and fraud

  • Helping the team improving other metrics like retention and active users

  • Helping the team with data-driven product decisions

Our requirements for this position are:

  • Experience in data analysis and visualization

  • Expert on Python (or R) for modeling and prototyping solutions

  • Experience with SQL and no-SQL databases

  • General Statistics Knowledge: Understands hypothesis testing (p-value), Understanding of key stat concepts like: mode, mean, standard deviation, statistical distribution types, discrete vs continuous variables, Bayes Theorem, Type i and Type ii errors, 

  • Advanced Stats: K-Neighborhood algorithm, decision tree based algorithms, T-tests, K-means, Boosting Algorithms

  • Can describe a metric in terms of inputs and outputs

  • Have done a project with an API to understand how APIs work and how to connect to them to gather data

  • Have worked with Software Engineers (that have scaled a model into a product)

  • Can run an application on the cloud

  • Attention to detail

  • Your data interpretation goes beyond the obvious

  • Problem solver

  • Shows hunger to learn new skills & tools to solve problems

  • Advanced English skills

  • Proactiveness and self motivation

Nice to have:

  • Previous experience building dashboards on tools like Metabase, Tableau, Power Bi or any other viz tools based on SQL

  • Experience with coding in other languages(C++, Java, C#, etc)

  • AWS experience

Bonus skills:

  • Have done projects with Graph Databases

  • Know Payments Industry

  • Have built models to detect fraudulent behaviour

  • Know Deep Learning

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