Senior data scientist, document intelligence

A fast‑moving, cross‑functional product team is seeking a senior data scientist to support an artificial intelligence–driven document intelligence product that extracts structured data from unstructured documents to power business‑to‑business quoting workflows. This role operates as a senior individual contributor with light leadership responsibility, combining hands‑on model development with squad‑level technical guidance.

The position is designed for a data scientist who thinks in terms of product outcomes, owns problems end to end, and collaborates closely with engineering and product partners in an iterative delivery environment.

Responsibilities

• Serve as a senior data science contributor within a cross‑functional squad focused on document intelligence and data extraction systems.
• Provide light technical leadership by guiding data science practices, prioritization, and execution within the squad.
• Design, build, and improve data extraction and AI‑driven pipelines that transform documents into structured data.
• Analyze data quality and model performance with a strong focus on precision, recall, and real‑world impact.
• Own experimentation cycles, including hypothesis development, testing, iteration, and performance evaluation.
• Partner closely with product and engineering to translate ambiguous business problems into scalable data science solutions.
• Contribute to continuous improvement of data science workflows, delivery quality, and modeling approaches.
• Leverage modern AI tools to accelerate development while maintaining strong foundational understanding of methods and results.
• Operate with high ownership and accountability, taking responsibility for outcomes rather than task completion.

Required experience and skills

Technical and analytical skills

• Strong, hands‑on proficiency in Python, including the ability to debug, explain, and maintain production‑quality code.
• Solid working knowledge of SQL for data manipulation, analysis, and exploration.
• Demonstrated strength in data analysis and problem exploration as a core differentiator.
• Practical experience applying data science and machine learning fundamentals in real‑world systems.

Applied machine learning and experimentation

• Experience working with document data, natural language processing, or similar unstructured data domains.
• Exposure to experimentation frameworks, model evaluation techniques, and iterative improvement practices.
• Familiarity with optimizing models for precision and recall.
• Awareness of reinforcement learning or adaptive systems as a future‑state capability.
• Experience with A and B testing concepts and experimentation approaches.

Collaboration and ways of working

• Strong sense of ownership and accountability for delivering meaningful product outcomes.
• Ability to work effectively across data science, engineering, and product disciplines.
• Comfort operating with limited direction and high autonomy.
• Proven ability to translate loosely defined problems into clear, actionable solutions.
• Willingness to work beyond traditional role boundaries to achieve squad goals.
• Curiosity‑driven mindset with a strong interest in experimentation and continuous learning.

Ideal background

• Senior‑level, full‑stack data scientist with experience across analysis, modeling, and delivery.
• Comfortable using AI‑assisted development tools without relying on them exclusively.
• Product‑oriented thinker who prioritizes business impact over isolated technical outputs.