John Deere Data Scientist in Indaiatuba, Brazil
Reconhecemos e valorizamos a pluralidade dos nossos times. Por isso, criamos condies para a equidade de oportunidades atravs de polticas, prticas e atitudes que promovam uma cultura de respeito e o desenvolvimento de talentos.
Buscamos cada vez mais diversidade de gnero, etnias, religies, LGBTQIA+, deficincias, culturas e histrias para impulsionar a inovao em nosso DNA.
E estamos sempre procura de mentes inovadoras que possam contribuir com a nossa misso. Que tal fazer parte do nosso time?
Local Principal: Brazil (BR)- So Paulo- Indaiatuba
Funo: Data & Analytics (CA)
Ttulo: Data Scientist- 88442
Would you like to play a part in the next digital transformation? As a Data Scientist II for John Deere Financial, you will be responsible for developing, delivering and validating predictive analytics models to increase and accelerate Data Science & Analytics capabilities throughout Latin America. As a Data Scientist II, you will use data science and analytics to better understand data patterns, identify opportunities, predict probability of default related to credit risk, among others. You will align with the JDF Risk Analytics team located in Johnston-Iowa, aiming to execute and deliver the analytics and insights needed to support digital transformation in JDF's Latin American business units. Join a team that is passionate about making a difference by applying data science techniques to solve problems and leverage data as an asset to develop insights and drive action. In addition, you will:
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 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
Research and leverage state of art modeling techniques, participate in modeling innovation
Work in a highly interactive, team-oriented environment with data scientists, data & analytics catalysts, analytics data engineers, data wranglers and visualization experts distributed across multiple countries
What Skills You Need
Microsoft Office skills are required (e.g. Excel, Powerpoint, Word, Outlook);
Advanced English level is mandatory (written and verbal skills);
Experience and programming skills in using data modelling software such as SAS, Python and/or R;
Strong ability to query data sources directly using SQL like query languages and/or PL/SQL programming;
Previous hands-on experience building predictive models through statistical modeling techniques. 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.
What Makes You Stand Out
Experience validating credit risk models and performing quantitative analyses for ongoing model improvement;
Previous experience with credit risk models (PD, LGD, and EAD) that support the loss estimation and stress testing;
Experience using visualization tools to present insights (e.g. Tableau, R Shiny, Data studio, Plotly and etc);
Demonstrated ability to function in a fast paced, collaborative team environment that is distributed across time zones and locations;
Experience working with Agile Project Teams, Scrum Teams or Product Teams.
O Que Voc Receber
Na John Deere, voc conseguir desenvolver uma carreira que levar voc aonde quer chegar. Alm de produzirmos mquinas conectadas e inteligentes, com a mais alta qualidade, criamos solues e servios que garantam que nossos clientes possam gerir seus negcios de forma sustentvel e produtiva. Com conexes tecnolgicas, sociais e humanas, voc ter liberdade de explorar novos projetos e pensar de forma inovadora.