Senior Data Scientist - Indaiatuba/SP

Participate in business partner meetings, brainstorming sessions, and workshops to capture business objectives and prioritize opportunities; Identify best analytical techniques that can be effectively applied to achieve business objectives and interpret the next best action through data driven insights; Build, test and deploy credit scoring and other types statistically-based predictive models; Maintain and enhance the existing models through various statistical tests; Develop, implement and analyze reports to keep business partners and management informed of key risk and performance information; Investigate potential areas of concern and communicate results of analysis to senior management; Work in a highly interactive, team-oriented environment with data scientists, data analytics catalysts, analytics data engineers and business intelligence analysts distributed across multiple countries; Bachelor's degree in quantitative field (e.g statistics, data science, mathematics, economics, computer science); Advanced English level is required (written and verbal skills); Proficiency in at least one of the following data science software tools (SAS, Python, PySpark, R, SQL); Analytical approach will include but is not limited to logistic regression, survival analysis, transition matrix, time series, neural networks, machine learning, among others; Experience performing statistical analysis of moderate to highly complex structured and unstructured data from multiple sources that may not have common identifiers; Previous hands-on experience building predictive models through statistical modeling techniques; Microsoft office skills are required (e.g. Excel, Powerpoint, Word, Outlook); Flexibility in schedule to work with colleagues and international customers across multiple countries; Experience validating credit risk models and performing quantitative analyses for ongoing model improvement; Experience using tools like Databricks, GitHub, etc; Experience using visualization tools to present insights (e.g. Tableau, R Shiny, Plotly, etc);