Data Scientist III

Company Description

Experian is a global data and technology company that powers opportunities for people and businesses around the world. We operate in diverse markets, such as financial services, healthcare, automotive, agribusiness, insurance, among others. Experian invests in people and new advanced technologies to unlock the power of data. We have an incredible team of 25,200 employees in 32 countries.

Our uniqueness is valuing yours. Experian’s people-centric, inclusive, and purpose-driven culture is recognized by numerous awards — including World’s Best Workplaces™ 2025 (Fortune’s Top 25 global) and Great Place To Work™ in 26 countries, among others. Check out Experian Life on social media or explore our careers site to understand why. Experian is also proud to be an equal opportunity and affirmative action employer.

Job Description

Job Description

We are looking for a Senior Data Scientist to join our Agribusiness Risk Management team. The professional will work in a highly qualified team of data scientists, focusing on the development of models, attributes, and analytical solutions that support strategic decisions and innovation in agribusiness.

This professional will also play a key role in defining analytical architecture, technical standards, and the direction of data science solutions, acting as a technical reference for the team and a bridge between business, engineering, and products.

Responsibilities

• Develop risk models that optimize the analysis and credit granting capacity in Agribusiness.
• Define technical approaches, stacks, and development standards for models and analytical pipelines.
• Document and communicate model results clearly and actionably for different stakeholders.
• Conduct presentations and gather feedback from internal and external clients.
• Collaborate with multidisciplinary teams, including professionals from Engineering and Product teams, to understand needs and identify possibilities for process and product improvements from a modeling perspective.
• Explore new variables and information to improve the predictive capacity of models.
• Evaluate and implement new techniques/technologies that enable improved efficiency and accuracy of risk models.
• Constantly monitor the alignment between technical decisions in data science and business objectives.
• Act as a technical reference (tech lead) for data scientists and analysts, supporting methodological decisions and code/model reviews.
• Lead the evolution of risk model architecture (features, validation, monitoring, MLOps).
• Evaluate trade-offs between complexity, performance, interpretability, and model governance.
• Support the technical planning of medium and long-term analytical initiatives.

Basic Requirements

• Degree in Data Science, Mathematics, Engineering, Statistics, Computer Science, or related fields.
• Proficiency in Python and experience with libraries such as Numpy, Pandas, Scikit-learn, Matplotlib, Seaborn, Jupyter, etc.
• Strong command of Machine Learning techniques applied to real-world risk/credit problems, including model validation, bias control, temporal stability, and explainability.
• Practical experience in designing complex features and evaluating the impact of variables in production.
• Ability to review, refactor, and guide analytical code for quality, efficiency, and reproducibility standards.
• Experience with risk modeling.
• Experience participating in analytical or technical architecture decisions (data, models, pipelines).
• Mastery in querying and manipulating databases for model construction and validation.
• Familiarity with version control tools, such as Git and Bitbucket, to manage and collaborate on code projects.
• Communication skills to present technical results clearly and convincingly.
• Ability to work independently and in a team, with excellent communication and interpersonal skills.

Desirable/Preferred

• Academic or professional experience in Agribusiness.
• Experience acting as a reference in data teams.
• Ability to mentor more junior professionals and influence technical decisions without formal authority.
• Experience with software engineering and Machine Learning engineering.
• Ability to evaluate the use of LLMs and AI agents as part of the analytical strategy.
• Experience working in agile and collaborative environments, with a focus on continuous value delivery.
• Knowledge of other programming languages, such as R, SQL, Scala, and Java.
• Knowledge of Hadoop, Spark/Pyspark, Polars, etc.
• Knowledge of Cloud environments (preferably AWS).

Qualifications

Qualifications

• Degree in Data Science, Mathematics, Engineering, Statistics, Computer Science, or related fields.
• Strong command of Machine Learning techniques applied to real-world risk/credit problems, including model validation, bias control, temporal stability, and explainability.
• Ability to review, refactor, and guide analytical code for quality, efficiency, and reproducibility standards.
• Experience with risk modeling.
• Experience participating in analytical or technical architecture decisions (data, models, pipelines).
• Ability to work independently and in a team, with excellent communication and interpersonal skills.

Additional Information

Serasa Experian is much more than you imagine. With the purpose of creating a better future by expanding opportunities for people and businesses, in Brazil we are more than 4,000 people working in diverse teams and specialties. Here, each knowledge and diversity complements each other and you can work on what you love most. We are committed to building an inclusive culture and an environment in which people can balance their careers with their personal commitments and interests, valuing well-being.

We are dedicated to being one of the best and most innovative companies to work for in the country, enabling incredible experiences and careers for our people. Our strong people-first approach is recognized externally through various market certifications: we have been awarded by Great Place To Work™ in 24 countries and by the international Top Employers certification, in addition to being recognized as one of the best companies for young professionals and having a 4.6 rating on Glassdoor. Each recognition indicates that we are on the right path, providing an increasingly better work environment for our talent.

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