Portcast is a venture-backed Singapore based startup that develops predictive supply chain technology for the logistics industry. We’re focused on building the next-gen logistics operating system to predict how cargo moves across the world and enable data-driven supply chain planning. Based out of Singapore, we’ve been building together since 2018, and are backed by some of the major investors in the tech industry, we believe that the logistics industry is at the inflection point of large-scale digitization.

Our mission is to transform international supply chains to be more resilient by helping logistics companies realize the full potential of their data. We cater to both shipping lines and cargo airlines, covering 90% of the world trade volume that travels via ocean and 35% of world trade value that travels via air. We use proprietary machine learning algorithms and real-time external market data (such as economic indices, marine weather, satellite-based data, etc.) to predict how much cargo will be shipped, when it will arrive, and deliver actionable insights.

You will be joining the lean Analytics team in this critical role of data management, quality assurance, and analytics. You will work on a data-heavy product that offers our customers end-to-end visibility of their cargo at each step of the ocean movement. Our prediction engine answers when, where, why, and by how many days the container would be delayed. You will ensure that the data ingested in the model and shared with the customers is error-free and of the highest quality. You will own internal performance dashboards and automation processes.

The right candidate would have experience in data analysis roles, preferably in a startup environment, and possess a high level of ownership, efficiency, and a data-driven mindset. You will be analytical, comfortable with a fast-moving organization, and eager to collaborate with different teams to drive impact across the organization.

• 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).

 

 To thrive in this role, you must have:

• 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.

• Good to have: basic understanding of machine learning algorithms.