Data Analyst

Job Summary

We're looking for a Data Analyst to join one of our R&D. You'll design and deliver data-driven solutions that combine data analysis, statistical methods, improved heuristics, and ML models training to power Beamup's autonomous supply chain intelligence platform. You'll work closely with data science, engineering, product, and supply chain experts — owning solutions from problem definition to iterative improvement.

Responsibilities

• Analyze large, multi-source operational datasets to identify trends, anomalies, and drivers of supply chain performance
• Improve and maintain heuristic rules and analytical models for supply chain use cases (e.g., inventory risk, demand patterns, root-cause analysis)
• Train and iterate on ML models to enhance existing analytical solutions
• Define success metrics and evaluation plans with support from senior DS/PM; run error analysis
• Work with stakeholders to create and maintain ground truth (label definitions, labeling workflows, QA checks, feedback loops)
• Implement and refine hybrid decision logic by combining analytical outputs with statistical methods and SQL/rules-based logic for robustness and explainability
• Collaborate with software engineers to support deployment of analytical solutions, including testing and logging
• Monitor deployed models/rules, investigate performance issues (data quality, drift, edge cases), and iterate based on outcomes
• Contribute to team practices: reproducible notebooks/code, documentation, and experiment tracking

Requirements

• BSc in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field (or equivalent practical experience)
• 2+ years of experience as a Data Analyst in a product or business environment (or equivalent practical experience)
• Familiarity with Python and data analysis workflows
• Solid SQL skills (joins, aggregations, window functions) and comfort working with production data in a warehouse/lake environment
• Experience performing rigorous evaluation (baselines, cross-validation where relevant, error analysis)
• Ability to translate business questions into measurable metrics and a clear analytical plan (with guidance when needed)
• Experience working with messy real-world data: data validation, debugging pipelines, and collaborating on labeling/ground truth
• Strong communication and collaboration skills with engineering, product, and domain experts; comfortable receiving feedback and iterating fast

Nice to Have (Advantages)

• Experience building predictive ML models (e.g., classification, regression, anomaly detection)
• Familiarity with common DS libraries: pandas, NumPy, scikit-learn
• Experience with big data tools (e.g., Spark, BigQuery, Snowflake, Databricks)
• Experience with development AI agents (Cursor, Caude-Code, etc.)