Data Scientist

About the Job Help serve America and Oceania region’s countries with data driven solutions. Data Scientists at Toyota Financial Services provide quantitative support, international regulatory standards compliance, and strategic perspective to the countries in the AOR region. This team acts as an internal consulting unit focused on the development, monitoring, and validation of models with high business impact and exposure. As part of the Data Science team, you will make critical recommendations to the different stakeholders across the region. Minimum qualifications: Bachelor’s degree in Economics, Statistics, Actuarial Science, Applied Mathematics, Data Science, Engineering, Physics, Finance, or a related quantitative field. 3+ years of work experience using analytics to solve product or business problems based on statistical analysis, statistical modeling, and coding (e.g. Python, R, SQL). Strong ability to communicate complex business problems and solutions in English and Spanish fluently to interact with regional and local stakeholders. 2+ Years of experience with statistical and quantitative modeling, forecasting, and machine learning techniques. Proactive mindset, international perspective, and openness to working on a hybrid model with occasional travel ( Preferred qualifications: Master’s degree or Ph.D. in Statistics, Mathematics, Data Science, Engineering, Physics, Economics or a related quantitative field. Version control techniques knowledge (GitHub, Git). Experience with analytics, data, and client focused solutions. Proven experience in validation process or end to end model development. Experience in Model Governance (MRM, ERM, etc.). Responsibilities Partnering with stakeholders to understand business challenges and deliver data-driven decisions. Presenting findings to clients and stakeholders with clarity and sensitivity to their needs, effectively translating technical concepts into business language. Validating predictive models (IFRS9, acquisition scorecard, collections scorecards) using advanced statistical and machine learning techniques. Participating in project planning and mentoring junior analysts on best practices. Ensuring quality standards and documentation of modeling processes. Commitment to continuous learning and professional development by staying up-to-date with emerging tools, technologies, and methodologies. Location : Mexico City (Hybrid model)

Creado: Jue, 01 de Ene de 1970