Data Scientist

Role Description

As a Data Scientist on our Applied Research team, you will build the intelligent systems that create the data our customers depend on. You will:

• Design extraction and classification models that process enterprise-scale document corpora.
• Build and evolve the entity resolution and signal detection layers powering the Commercial Graph and Financial Graph.
• Define how AI capabilities surface as recommendations, agents, and search across the platform.
• Own the models, pipelines, and graph structures that are the product.
• Work directly with engineering, product, and customers on problems where a single clause can represent tens of millions of dollars of exposure.
• Ensure model accuracy has a contractual SLA.

Qualifications

• 5+ years of experience in data science, applied ML, or AI research with production-shipped systems, not just notebooks and prototypes.
• Strong statistical foundations and the ability to define and evaluate success metrics for AI systems including precision, recall, coverage, latency, not just accuracy.
• Deep experience building NLP, NLU, or document understanding models that operate on messy, real-world unstructured data at scale.
• Strong intuition for entity resolution, knowledge graph construction, or graph-based modeling.
• Hands-on proficiency in Python and modern AI frameworks, with experience deploying models into production pipelines.
• Comfort with information extraction, classification, and retrieval-augmented generation patterns applied to real enterprise workloads.
• A track record of working cross-functionally with engineering and product to shape what gets built.
• Clear, structured communication skills.
• High ownership mentality regarding model quality, pipeline reliability, and customer outcomes.

Requirements

• Experience building or evolving knowledge graphs, commercial ontologies, or financial data models in enterprise contexts.
• Prior work on document AI, OCR pipelines, or hybrid extraction systems combining rule-based and learned approaches.
• Exposure to AI agent architectures, tool-use patterns, or autonomous reasoning systems in production.
• Background in procurement, contract management, spend analytics, or financial operations domains.
• Experience with evaluation frameworks for AI systems (RAGAS, custom eval harnesses, human-in-the-loop QA pipelines).
• Familiarity with distributed data platforms, event-driven architectures, or streaming systems (Ray, Kafka, Azure Service Bus).
• Prior work at a high-growth startup or enterprise AI company.
• An MS or PhD in a quantitative field.

Benefits

• Competitive salary.
• Annual performance bonus.
• Employee stock option plan.
• 100% paid medical, dental, and vision coverage.
• 401(k) with employer contribution.
• Generous vacation and sick leave.
• Flexible work arrangements.
• High-quality equipment for home and office.
• Strong culture of collaboration, mentorship, and continuous improvement.