Data Analyst

Job description

Portcast is a venture-backed, Singapore-based logistics technology startup building a real-time transportation visibility platform for global supply chains. We help shippers, manufacturers, and logistics service providers turn data into decisions and decisions into measurable business impact. Our platform goes beyond visibility. Portcast enables action at scale by surfacing the right risks early, helping teams prevent detention and demurrage, accelerate exception management, and close invoices faster with built-in evidence. We turn visibility into outcomes: reduced costs, improved operational control, and more predictable supply chains. Founded in 2018 and backed by leading technology investors, we are building for an industry at a critical inflection point of digital transformation. Our team of software engineers, data scientists, and logistics experts is on a mission to make supply chains not just visible, but decisively actionable: end to end. **ABOUT THE ROLE:** Portcast has already been used by global freight forwarding companies, manufacturers and cargo airlines. Our customers trust us to deliver fast, reliable and secure predictions, our engine answers when, where, why, and by how many days a container will be delayed. That trust lives or dies on data quality, and that's what you'll own. As our Data Analyst, you will be reporting directly to our Analytics Manager and work closely with our Data Science, Engineering, Product, and Commercial teams. You'll sit on a data-heavy product that gives customers end-to-end visibility of their cargo across the ocean journey, making sure the data we ingest into the models and share with customers is error-free and of the highest quality. You won't just run the query and close the ticket here. When an anomaly shows up, you'll chase why it's there, trace it to the real source, and fix the process so it doesn't come back. Curiosity isn't a nice-to-have on this team, it's how we build. ### WHAT YOU'LL OWN: - Documentation, reporting, and root-cause analysis of prediction issues: owning the problem end to end, not just flagging it. - Analyze ingested and system-generated data for anomalies and gaps, and refer to various data sources to plug those gaps. - Turn data into stories: surface the pattern behind an issue, propose the fix (QA process-flow change and/or automation), and own the subsequent plan and execution. - Automate performance/accuracy review processes, report generation, and data visualization using Python and SQL: building reusable, scalable workflows, not one-off scripts. - Support Engineering and Data Science on system-level data fixes. Understand how the engine makes predictions, and explore improvements by fixing or introducing new data and features. - Support Customer Operations and Marketing with insights from the data: performance, accuracy metrics, the impact of real-time events, as needed. - Maintain and own internal and customer dashboards based on trial/account requirements (prediction accuracy, timeliness, coverage, explainability). ### WHAT WE'RE LOOKING FOR: - 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. - 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. We treat AI as a tool that makes good thinking faster, not a shortcut around thinking. - 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. ### WHAT'S IN IT FOR YOU: - Globally distributed, remote-first flexibility: Work with a fully distributed team across Asia and Europe, built on trust, accountability, and collaboration. Our diversity of perspectives fuels innovation and keeps us curious. - Data-first team: You'll work with like-minded individuals who share a passion for solving difficult problems with data. - Accelerated growth: Compress the learning curve in a couple of years by owning data quality and analytics from day one as your own baby. We are building our company to be the next B2B market leader in predictive global supply chains, and you'll be a major part of our story. - Impact you can see: With a lean structure, your work is effective from the start. You'll see the results of your ideas and decisions directly moving the business forward. ### Our CORE Values Guide Everything We Do: - Curiosity: We read the data before we trust it. We dig into why a number looks the way it does, not just how to make the anomaly disappear. - Ownership: We act like founders. We don't wait for someone to flag a data issue, and we stay on a problem until it's actually fixed at the source. - Raising the bar: We don't settle for a report that only works this once. We aim for data pipelines and dashboards that are reliable, reusable, and easy for the team to trust. - Effectiveness: We focus on analysis that creates real product and customer impact. We prioritize the right problems, make practical trade-offs, and ship work that improves outcomes, not just output.