Data Scientist - Data and Analytics (CA)

As a Data Scientist on the John Deere Financial (JDF) Global Operational Analytics (GOA) team, based in Indaiatuba/SP  you will primarily support the JDF Global Fraud team through the maintenance, validation, enhancement, and development of risk-based analytical models. You will partner with business, analytic, and technical stakeholders to translate evolving fraud risks and operational needs into analytical capabilities that improve fraud detection, operational work prioritization, and overall decision making.

Beyond Global Fraud, this role also provides analytical support across other domains within the GOA team’s responsibilities, including maintaining existing scenario analysis capabilities and supporting their evolution as business needs emerge. Working across a global stakeholder network, you will contribute across the analytics lifecycle, from data preparation and analysis through model implementation, monitoring, documentation, and continuous improvement.

ENGLISH IS REQUIRED FOR ALL COMMUNICATIONS AND DOCUMENTS (Resume)

Main Responsibilities

• Maintain, assess, validate, and enhance risk-based models supporting Global Fraud detection, operational prioritization, and decision making;

• Develop and apply analytical models and methods to address evolving fraud risks and business needs;

• Analyze data to identify patterns, anomalies, emerging risks, and opportunities to improve model effectiveness;

• Partner with Global Fraud, GOA leadership, and other business and technical stakeholders to understand business questions, define analytical requirements, and establish measures of success;

• Build and maintain repeatable data preparation, testing, model assessment, and validation processes;

• Support model monitoring, documentation, controls, and knowledge management to improve transparency, reliability, and long-term maintainability;

• Maintain and support scenario analysis capabilities across other domains within GOA’s analytical responsibilities as business needs require;

• Communicate analytical methods, findings, limitations, and recommendations clearly to technical and non-technical stakeholders;

• Collaborate with data engineers, business intelligence analysts, GOA leadership, and senior technical team members throughout the analytics product lifecycle;

• Research relevant analytical methods, technology, fraud trends, and industry practices and recommend improvements to analytical capabilities and ways of working.

Skills You Need

• Bachelor’s degree or equivalent experience in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Finance, Analytics, or a related quantitative field;

• Advanced English: broad vocabulary, strong grammar, and the ability to lead meetings, debate alternatives, negotiate requirements, and present to stakeholders and leaders;

• Experience applying statistical analysis, predictive modeling, machine learning, or related analytical methods to business problems;

• Experience with Python, PySpark, R, SQL, or comparable, with the ability to prepare, analyze, validate, and interpret data;

• Strong analytical reasoning and problem-solving skills, including an understanding of model assessment, validation, monitoring, and performance measurement;

• Ability to work effectively with global business stakeholders and technical teams.

What Makes You Stand Out

• Knowledge of financial services, fraud risk, credit risk, operational risk, or investigative analytics;

• Experience developing or maintaining fraud detection, risk scoring, anomaly detection, or other risk-based models;

• Experience with Databricks, MLflow, cloud-based analytics platforms, or model monitoring capabilities;

• Experience with scenario analysis or other analytical decision-support capabilities;

• Experience evaluating large language model or generative AI performance, including developing evaluation approaches and assessing output quality across dimensions such as correctness, relevance, groundedness, and task performance;

• Demonstrated ability to translate analytical outputs into practical business decisions and continuously expand business and technical expertise.