Senior Data Scientist

Role Description

As a member of the BEES Logistics Data Science team, you will develop data-driven solutions that power delivery planning, operational efficiency, and customer promise accuracy across multiple markets.

• Be part of a high-impact data science team building intelligent logistics systems that optimize delivery operations at a global scale.
• Design, develop, and deploy machine learning models and optimization solutions across the full lifecycle — from research and experimentation to production — focusing on planning, forecasting, and operational decision-making.
• Apply advanced techniques such as statistical modeling, optimization, geospatial analytics, and forecasting to improve efficiency, reliability, and cost of delivery operations.
• Translate complex real-world logistics constraints into scalable mathematical models and data-driven systems.
• Contribute to experimentation and performance evaluation through offline analysis and online testing, ensuring solutions are robust, scalable, and aligned with operational goals.
• Write production-grade code and build reusable data and modeling pipelines that operate reliably at scale.
• Collaborate closely with engineers, product managers, operations teams, and business stakeholders to deliver impactful solutions.
• Drive continuous improvement by exploring new methodologies in machine learning, optimization, and applied statistics, raising the technical bar across the organization.

Qualifications

• Strong foundation in mathematics, statistics, and problem solving.
• Bachelor’s degree in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative field; Master’s preferred; PhD is a plus.
• Proven experience applying machine learning, optimization, or advanced analytics to real-world problems in production environments.
• Experience with complex systems involving uncertainty, constraints, and large-scale data.
• Proficiency in Python for data analysis, modeling, and production workflows; experience with distributed processing (e.g., Spark / PySpark) is a plus.
• Familiarity with at least one of the following domains: optimization, forecasting, geospatial analytics, or large-scale operational systems.
• Experience with experimentation frameworks, model validation, and performance monitoring.
• Strong understanding of software engineering best practices, including version control, CI/CD, and reproducible workflows.
• Ability to work with ambiguity, break down complex problems, and deliver practical, high-impact solutions.
• Excellent communication skills, with the ability to explain technical concepts to both technical and non-technical audiences.

Benefits

• Performance based bonus*
• Attendance Bonus*
• Private pension plan
• Meal Allowance
• Casual office and dress code
• Days off*
• Health, dental, and life insurance
• Medicines discounts
• WellHub partnership
• Childcare subsidies
• Discounts on Ambev products*
• Clube Ben partnership
• Scholarship*
• School materials assurance
• Language and training platforms
• Transport allowance