Data Analyst

Summary

Join our lean Analytics team as a Data Analyst and contribute to data management, quality assurance, and analytics in our predictive supply chain technology. You will work on a data-heavy product that offers end-to-end visibility of cargo movement, ensuring error-free data and high-quality insights for customers.

Requirements

• Bachelor's or Master’s degree in Computer Science, Engineering, Information Technology, or a related field
• Prior experience in data analysis or data analyst roles, preferably in a startup environment
• Exceptionally skilled in Python and SQL, with a demonstrated ability to consistently produce reusable and highly scalable code
• An eye for detail: Looking for anomalies in the system
• Familiarity with Linux, GitHub, product development
• Proven experience with data visualization
• Empathy and Urgency: to feel the customer pain and react promptly on a day-to-day basis
• A Problem solver & go-getter: either programmatically or manually meeting customer expectations and delivering on time
• Fluent with written and verbal in English
• Strong ownership mindset, efficiency, and data-driven approach

Responsibilities

• Documentation, Reporting, Root Cause analysis of Prediction issues
• Analyze ingested and system-generated data for anomalies and gaps
• Refer to various data sources to plug those gaps
• Create data stories and come up with possible solutions in terms of QA process flow change and/or automation. Own the subsequent plan and execution
• Automation of Performance/Accuracy review processes, report generation, data visualization using Python, SQL
• Support engineering and data science teams in system-level data fixes. Understand how the engine makes predictions, explore possible improvements in the process by fixing/introducing new data/features
• Support the customer operations, marketing team in getting insights from the data, such as performance, accuracy metrics, impact of real-time events, etc. as required
• Maintain, own internal and customer dashboards based on the trial/account requirements (e.g. prediction accuracy, timeliness, coverage, explainability)