Senior AI & Data Engineer - Strategy & Operational Intelligence

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.

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.

This is one role with two modes, and you’ll move between them constantly, often inside the same week.

• 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.
• Insight presentations and data narratives for technical and non-technical audiences.
• Data quality, documentation, and engineering standards across your work.
• Internal process improvements that automate manual data work and improve scalability.
• Adoption and impact of your models and dashboards after launch, not just delivery.

• →Translate business and operational problems into scoped data products with clear requirements, users, and success metrics.
• →Own and prioritise the data science backlog with stakeholders, and decide what to build, defer, or drop.
• →Mine complex, large-scale datasets to uncover hidden patterns, fee anomalies, and cross-domain correlations.
• →Surface insights to leadership, operations, and partner teams before the questions are asked.
• →Build analytics tools and dashboards in Tableau, Power BI, Matplotlib, and Seaborn that turn complex data into action.
• →Present findings clearly and confidently to both technical and non-technical audiences.
• →Design, train, and deploy scalable ML and DL models for NLP and predictive analytics use cases.
• →Apply current modelling techniques to logistics and operational problems across the PackageX platform.
• →Architect and maintain ETL pipelines, data warehouses, and data models.
• →Automate manual workflows, optimise data delivery, and redesign infrastructure for scale.
• →Build and expose data services via RESTful APIs using Django or Flask, deployed on AWS or GCP with Docker.
• →Work with SQL Server, MongoDB, and other databases to manage enterprise-scale datasets.
• →Follow engineering best practices: unit testing, design and code reviews, documentation, and CI/CD.
• →Track adoption and business impact of shipped models and dashboards, and iterate or retire what isn’t used.
• →Partner with data analysts, ML engineers, and business stakeholders so models serve both technical and strategic needs.
• →Turn around bug fixes and enhancement requests quickly, and deliver on time with minimal supervision.

• 5+ years as a data scientist, ML engineer, or data engineer working on production-grade systems.
• Expert-level Python (Pandas, NumPy) and SQL for data processing, analysis, and complex querying.
• Hands-on experience with TensorFlow, Keras, and/or PyTorch.
• Deep understanding of NLP and modern deep learning algorithms.
• A track record of finding insights that changed a business decision, not only building models to spec.

• Strong backend experience with Django and Flask, including building and integrating RESTful APIs.
• Experience with data warehousing, ETL pipeline design, and data modelling across relational, NoSQL, and large-scale datasets.
• Experience with GitHub, AWS, GCP, Docker, and CI/CD tooling.
• Experience developing and integrating Tableau and/or Power BI, plus fluency with Matplotlib, Seaborn, and similar libraries.

• Strong communication with business leaders, product teams, and operations, not only other data people.
• Proactive and self-directed: you go looking for the question, not just the answer.
• Comfortable creating structure in fast-moving, ambiguous environments.
• Strong curiosity about logistics, supply chain, warehousing, and physical operations.