Data Scientist

About you

Our Data Scientist will be responsible for discovering and making value out of a vast amount of data. As an essential part of Data Area and the process of analyzing, visualizing and modeling Data for the business, the position needs to identify valuable information from a several amount of sources, do statistical analysis and build high-quality predictive models to be integrated with our products. A successful Data Scientist should be a critical and analytical thinker, with the ability to think as a customer, work in a team and communicate effectively.

Responsibilities
  • Work with stakeholders to identify opportunities for leveraging company data to drive business solutions.

  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.

  • Assess the effectiveness and accuracy of new data sources and data gathering techniques.

  • Develop custom data models and algorithms to apply to data sets.

  • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.

  • Develop company A/B testing framework and test model quality.

  • Coordinate with different functional teams to implement models and monitor outcomes.

  • Develop processes and tools to monitor and analyze model performance and data accuracy.

Experience
  • 3+ years of experience in data manipulation and scripting with Python/MongoDB/SQL

  • Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.

  • Experience working with and creating data architectures.

  • Fintech domain experience and expertise.

  • Startup leadership position is a huge plus.

Requirements
  • Strong problem solving skills with an emphasis on product development.

  • Excellent written and verbal communication skills for cross-teams coordination.

  • Experience working with and creating data architectures.

  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, regression, etc.) and their real-world advantages/drawbacks.

  • Knowledge of advanced statistical techniques and concepts (properties of distributions, statistical tests and proper usage, etc.) and experience with applications.

  • A drive to learn and master new technologies and techniques.

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