Company Description
Serasa Experian is Brazil's first and largest Datatech company. A leader in intelligence solutions for risk analysis and opportunity identification, with a focus on credit journeys, authentication, and fraud prevention. With cutting-edge technology, innovation, and the best talent, we transform risk uncertainty into the best decision, helping people achieve their dreams and companies of all sizes and segments to thrive.
We have 22,000 people operating in 32 countries and every day we are investing in new technologies, talented professionals, and innovation to help all our clients maximize every opportunity. With corporate headquarters in Dublin, Ireland, Experian is listed on the London Stock Exchange (EXPN) and is part of the FTSE 100 index.
Job Description
Job Description
We are looking for a Senior Data Scientist to join our Risk Management team in Agribusiness. The professional will work in a highly qualified team of data scientists, developing 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 directing 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 capacity for analysis and credit granting in Agribusiness.
- Define technical approaches, stacks, and standards for model development and analytical pipelines.
- Document and communicate model results clearly and actionably to different stakeholders.
- Conduct presentations and gather feedback from internal and external customers.
- Collaborate with multidisciplinary teams, including professionals from Engineering and Products teams, to understand needs, identify opportunities 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 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 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 variable impact in production.
- Ability to review, refactor, and guide analytical code to quality, efficiency, and reproducibility standards.
- Experience with risk modeling.
- Experience participating in analytical or technical architecture decisions (data, models, pipelines).
- Proficiency in querying and manipulating databases for model building 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/Differentiators
- Academic or professional experience in Agribusiness.
- Experience acting as a reference in data teams.
- Ability to mentor 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 to 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, expanding opportunities for people and companies, in Brazil we have more than 4,000 people working in diverse teams and specialties. Here, every knowledge and diversity complement 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, prioritizing well-being.
We are very 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, and we are recognized as one of the best companies for young professionals with 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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