Senior AI & Data Engineer - Strategy & Operational Intelligence

About PackageX
PackageX is building the Physical Logistics Execution Platform for enterprise teams operating across manufacturing sites, distribution centers, warehouses, retail locations, and logistics-heavy environments.
Our platform uses Vision AI, workflow automation, mobile execution, and integrations to help companies digitize, execute, and verify physical operations at the point of work, creating trusted evidence of what happened across their operations.
Our operating model is simple: Execution. Verification. Evidence.
Role Overview
PackageX is looking for a Senior AI & Data Engineer - Strategy & Operational Intelligence who doesn’t just build models, but digs into the data to find what others miss: hidden patterns, fee and cost anomalies, and connections across datasets that change how the business makes decisions.
This is not a role for someone who maintains predefined dashboards. It’s for someone who asks “why” before “how”, spots patterns before they become obvious, and can turn raw data into a clear story for leadership, product, and operations teams.
Working across our platform data, customer operations, and the workflows running on warehouse and receiving floors, you’ll mine large datasets, build and deploy ML models, design the pipelines that feed them, and put the findings in front of the people who need to act on them.
This is a senior, hands-on role. The right person is a rigorous data scientist, a capable engineer, and a strong communicator at the same time. This is more than analysis or model building. You’ll be a strategic intelligence partner across the company, and you’ll own your work from raw data to production system to boardroom slide.
You’ll also bring a technical product owner mindset. That means deciding which problems are worth solving, defining what success looks like before you build, and treating every model, pipeline, and dashboard as a product with users, a roadmap, and adoption to earn.
How This Role Works
This is one role with two modes, and you’ll move between them constantly, often inside the same week.
Insight mode. You are in the data: mining operational and platform datasets, finding anomalies and correlations, building the analysis and dashboards that explain them, and presenting what you found to leadership, operations, and partner teams before they think to ask.
Build mode. You are in the code: designing, training, and deploying ML and NLP models, building the ETL pipelines and data models underneath them, and exposing them as production services the platform can rely on.
Across both, think like a product owner. Before you build, you define the business problem, the user, and the metric that proves it worked. You prioritise ruthlessly, say no to work that won’t move a decision, and stay with what you shipped until people actually use it.
Both halves are required. Shipping production ML systems is a gate for this role, not a bonus, and so is the ability to find and communicate business insight. If you are a strong modeller who has never deployed to production, an analyst who has only worked from someone else’s dashboard spec, or someone who needs a PM to tell you what to build next, this is not the right role.
What You’ll Own
Roadmap and prioritisation for data science and ML work, based on business impact.
Problem definition, requirements, and success metrics for every data product you build.
Insight generation across operational, customer, and platform data.
Detection of fee anomalies, cost drivers, and cross-domain patterns that affect business decisions.
ML and deep learning models across NLP and predictive analytics, from design through deployment.
Frameworks and services for ML systems that are reproducible, performant, and production-ready.
ETL pipelines, data warehouses, and data models that power analytics at scale.
Data services exposed through RESTful APIs and deployed on AWS or GCP.
Analytics tools and dashboards used by leadership, operations, and partner teams.
Insig

Employment type
Full Time

Work arrangement
No