Data Scientist - Focus on SAP MM Procurement

BrazilRemoteFull-timemid

Data ScientistData

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Quick Summary

Overview

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data Scientist – Focus on SAP MM Procurement based in Brazil.

Technical Tools

Data ScientistData

Join an innovative technology environment where data science, artificial intelligence, and enterprise software converge to transform global procurement operations. In this role, you will design and develop intelligent solutions that combine machine learning, generative AI, and SAP technologies to solve real-world business challenges. You will help integrate AI capabilities into existing procurement applications while creating new, AI-powered experiences that deliver measurable business value. Working alongside software engineers, product teams, and business stakeholders, you will turn complex requirements into scalable, reliable solutions. You will also contribute to modern engineering practices, MLOps, data quality, and responsible AI governance. This opportunity is ideal for a technically curious professional who enjoys experimentation, continuous learning, and shaping the future of AI-driven enterprise solutions.

Design, develop, and implement artificial intelligence and machine learning capabilities for global procurement solutions.
Integrate AI functionality into existing SAP Materials Management (SAP MM) applications and contribute to the development of new AI-powered user experiences.
Leverage data, APIs, enterprise systems, and cloud technologies to translate business challenges into scalable technical solutions.
Explore, develop, and implement generative AI, Large Language Model (LLM), and Retrieval-Augmented Generation (RAG) solutions tailored to procurement use cases.
Contribute to application architecture, software evolution, and the adoption of modern AI-first engineering approaches.
Collaborate with software engineers, product teams, and business stakeholders to understand user needs and deliver effective technology solutions.
Support the implementation of best practices in software engineering, AI development, MLOps, data quality, and data governance.
Experiment with emerging technologies, challenge conventional approaches, and identify opportunities to improve procurement processes through intelligent automation.
Help ensure AI-powered enterprise solutions are scalable, reliable, maintainable, and aligned with responsible AI principles.

Requirements

~2 min read

Bachelor's degree or equivalent higher education qualification in Computer Science, Software Engineering, Data Science, Information Systems, or a related discipline.
Advanced English proficiency, with the ability to communicate effectively and collaborate in an international working environment.
Availability to work in a hybrid model, with on-site presence in Indaiatuba, São Paulo, three days per week.
Practical experience with Python, SQL, statistics, probability, and machine learning concepts.
Knowledge of data engineering fundamentals, including experience or familiarity with Databricks, Apache Spark, and cloud platforms such as Microsoft Azure or AWS.
Experience with generative AI, LLMs, RAG architectures, and API integrations.
Familiarity with MLOps practices, data visualization tools such as Power BI or Tableau, data governance, data quality, Git, and GitHub.
Strong analytical and problem-solving skills, with the ability to translate business requirements into practical technical solutions.
Collaborative communication skills and the ability to work effectively with multidisciplinary teams in a global environment.
Adaptability, curiosity, and a willingness to experiment with emerging technologies and continuously improve engineering practices.

Preferred qualifications:

Experience with multi-agent systems, AI solution architecture, or Natural Language Processing (NLP).
Knowledge of vector databases and search technologies such as OpenSearch, Pinecone, or Azure AI Search.
Experience with deep learning, containerization, and software architecture principles or design patterns.
Understanding of responsible AI, AI governance, and the development of scalable AI-powered enterprise applications.