Financial Data Scientist Jobs in Oregon (NOW HIRING)

Location: US

Level: Senior Individual Contributor

Team: Engineering

Terzo builds an AI-native enterprise data platform designed to power the commercial and financial operating system of modern companies. The platform transforms complex, unstructured enterprise data into structured, actionable intelligence used directly in operational and financial decision-making. Terzo sits at the intersection of data platforms, AI systems, and enterprise software, focusing on real production use cases rather than demos or point solutions.

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, and define how AI capabilities surface as recommendations, agents, and search across the platform. You will own the models, pipelines, and graph structures that are the product - working directly with engineering, product, and customers on problems where a single clause can represent tens of millions of dollars of exposure and where model accuracy has a contractual SLA.

• 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 and you've thought seriously about how to connect fragmented data into structured, queryable representations
• 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, not just executing on handed-down specs
• Clear, structured communication where you can explain a model decision to a PM, defend an architectural choice to a staff engineer, and present results to leadership without hiding behind jargon
• High ownership mentality where you treat model quality, pipeline reliability, and customer outcomes as your responsibility

• 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

• Opportunity to build and own a foundational enterprise data platform
• High-impact role with real influence on architecture and technical direction
• Complex problems involving data, AI, scale, and enterprise customers
• Small, senior team with strong ownership and minimal bureaucracy
• Clear runway for technical and leadership growth as the platform scales

• 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