Data Analyst

About Us
Beamup helps enterprises move beyond dashboards, alerts, and manual triage to automated supply chain execution.
Our AI platform supports large, complex retail and manufacturing networks by deploying specialized AI agents that operate continuously across stores, distribution centers, warehouses, and in-transit operations. These agents detect execution issues, identify root causes, and execute corrective actions - either autonomously or by routing work to the right teams.
Retailers and manufacturers rely on Beamup to replace reactive workflows with consistent, scalable execution — reducing losses, improving performance, and operating with greater confidence at global scale.

Our Mission
To redefine supply chain intelligence by building AI agents that predict, prevent, and resolve inventory health and operational issues in real time.

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.)

Beamup is proud to be an equal opportunity employer and provides equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or genetics.

You can read more about us at:

https://www.beamup.ai/

And we were also chosen to be one of the 50 most promising Israeli startups of 2023:
https://www.calcalistech.com/ctechnews/article/hjtwkugx2