Senior Data Scientist - BEES Logistics
New
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonMachine LearningData scienceSparkSoftware EngineeringPySpark
Requirements
- Strong foundation in mathematics, statistics, computer science, or related quantitative fields (Master’s or PhD preferred).
- Proven experience applying machine learning, optimization, or advanced analytics in production environments.
- Strong Python programming skills for data analysis, modeling, and production workflows.
- Experience with large-scale or complex systems involving uncertainty, constraints, and high-volume data.
- Familiarity with at least one core domain: optimization, forecasting, geospatial analytics, or operational systems.
- Experience with experimentation frameworks, model validation, and performance monitoring.
- Strong understanding of software engineering best practices, including version control and reproducible workflows.
- Experience with distributed data processing tools (e.g., Spark/PySpark) is a plus.
- Strong analytical thinking, autonomy, and ability to work with ambiguity.
- Excellent communication skills, with the ability to explain technical concepts to diverse audiences.
Responsibilities
- Design, develop, and deploy machine learning models and optimization solutions to improve logistics planning, forecasting, and operational decision-making.
- Translate complex logistics constraints into scalable mathematical, statistical, and data-driven models.
- Build production-ready data pipelines and reusable modeling frameworks to support large-scale deployment.
- Apply advanced techniques such as forecasting, optimization, and geospatial analytics to improve delivery performance and efficiency.
- Conduct experimentation, offline evaluation, and online testing to validate model performance and business impact.
- Collaborate with engineers, product managers, and operations teams to deliver end-to-end data science solutions.
- Contribute to continuous improvement by exploring and applying new methodologies in machine learning and applied statistics.
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