Data Analyst

Batch: 2020/2021/2022/2023/2024. Qualifications: - Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field - At least 2-3+ years in a Data Analyst or Business Analyst role, preferably in a product-based, lean startup particularly in logistics, ecommerce or B2B SaaS data-heavy domain Required Skills: - Exceptionally skilled in Python and SQL, with a demonstrated ability to consistently produce reusable and highly scalable code - An eye for detail: you go looking for anomalies in the system before a customer does, and proven experience turning messy data into clear visualizations - First Principles Thinker: when you explain a past decision, the trade-offs come out before the tools. You name the constraint, the alternatives you considered, and why you picked what you picked. You don't reach for a new tool when a tighter use of an existing one would do the job - Curious by Default: you dig deeper instead of following instructions to the letter. When the data surprises you, you chase the why, not just the fix, and you ask the good questions early - You own your output, AI included. You use AI tools (Claude, Copilot, etc.) where they make you faster on QA, automation, and analysis, and you know exactly where they fall short. You read and validate what they produce — you don't just paste it. You can explain the anomaly, the root cause, and the fix without saying "the AI found it," and you flag AI-assisted work without being asked - Empathy and Urgency: you feel the customer's pain and react promptly on a day-to-day basis (either programmatically or manually) to meet expectations and deliver on time - You communicate nuanced ideas clearly, from real-time remote brainstorming to explaining a data decision in writing. You may be opinionated, but in disagreements you engage thoughtfully with other perspectives and can compromise when needed - You have experience working in a diverse, dynamic, and cross-cultural team, with a strong ownership mindset, efficiency, and a data-driven approach Good to Have: - A basic understanding of machine learning algorithms