Senior Data Scientist

About PackageX

PackageX is the execution layer for physical AI in enterprise logistics. We enable enterprises to see, understand, and execute real-world logistics operations in real time using Vision–Language–Action intelligence.

We sit between systems of record (ERP, WMS, TMS) and the physical world, turning cameras, people, and automation into deterministic, auditable workflows. Rather than replacing existing systems, PackageX makes them operationally intelligent.

We start with inbound receiving, the highest-friction choke point in logistics, and expand across adjacent workflows, sites, and automation modes. This creates a workflow-led enterprise expansion motion that compounds inside complex operations.

We're a fast-growing pre-Series A stage startup in New York City with a distributed global team backed by Bullpen Capital, Pritzker Group, Sierra Ventures, Ludlow Ventures, MXV Capital, and NSV Wolf Capital.

The Opportunity:

We’re looking for a hands-on data scientist someone who doesn’t just build models, but digs deep to uncover fee insights, connects disparate data points, and surfaces extraordinary, business-shifting intelligence that others simply miss.

This is not a role for someone who executes on predefined dashboards. It’s for someone who asks “why” before “how”, who sees patterns before they become obvious, and who can translate raw data into a compelling narrative for business leaders, product teams, and operations alike.

You will be a strategic intelligence partner across the company, combining rigorous data science, creative analytical thinking, and outstanding communication to move the business forward.

What You’ll Do:

Data Insights & Business Intelligence

• Mine complex, large-scale datasets to uncover hidden patterns, fee anomalies, and cross-domain correlations that drive real business decisions.
• Proactively identify and surface extraordinary insights for leadership, operations, and partner teams, before the questions are even asked.
• Build intuitive analytics tools and dashboards (Tableau, Power BI, Matplotlib/Seaborn) that translate complex data into actionable intelligence.
• Deliver insight presentations with clarity and confidence to both technical and non-technical audiences.

Machine Learning & Predictive Modeling

• Design, train, and deploy scalable ML/DL models across NLP and predictive analytics domains.
• Build frameworks and services for Machine Learning systems, ensuring reproducibility, performance, and production readiness.
• Apply state-of-the-art modeling techniques to solve logistics and operational challenges across the PackageX platform.

Data Engineering & Infrastructure

• Architect and maintain ETL pipelines, data warehouses, and data models that power analytics at scale.
• Identify and implement internal process improvements: automating manual workflows, optimizing data delivery, and redesigning infrastructure for greater scalability.
• Build and expose data services via RESTful APIs using Django or Flask; deploy on AWS/GCP with Docker-based containerization.
• Work with SQL Server, MongoDB, and other DBMS platforms to manage complex, enterprise-scale datasets.

Quality & Collaboration

• Follow engineering best practices: unit testing, design/code reviews, documentation, and CI/CD pipelines.
• Collaborate closely with data analysts, ML engineers, and business stakeholders to ensure data models support both technical and strategic needs.
• Respond quickly to bug fixes and enhancement requests; deliver on time with minimal supervision.

What We’re Looking For:

• 5+ years of experience as a data scientist working on production-grade systems.
• Expert-level proficiency in Python (Pandas, NumPy) and SQL for data processing, analysis, and complex querying.
• Hands-on experience with ML/DL frameworks: TensorFlow, Keras, and/or PyTorch.
• Deep understanding of NLP and modern deep learning algorithms.
• Extensive backend experience with Django and Flask; comfort building and integrating RESTful APIs.
• Experience with GitHub, AWS, GCP, Docker, and CI/CD tooling.
• Proficiency with data warehousing, ETL pipeline design, and data modeling (schemas, NoSQL, large-scale datasets).
• Experience with enterprise BI tools: Tableau and/or Power BI integration and development.
• Fluency in Python visualization stack: Matplotlib, Seaborn, and similar libraries.