Data Scientist

Terzo

Data Scientist responsible for designing extraction and classification models for Terzo's AI-native enterprise data platform. Collaborating with teams to develop systems that enhance data intelligence.

Posted 7/27/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying AI models, particularly in NLP and document understanding, while ensuring model accuracy and reliability. Strong ability to communicate complex model decisions and collaborate cross-functionally to drive impactful outcomes.

Highest-signal resume keywords

Data Science ExperienceNLP Model DevelopmentPython ProficiencyEntity Resolution ExpertiseKnowledge Graph Construction

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical FoundationsModel Evaluation MetricsInformation ExtractionClassification ModelsDocument UnderstandingGraph-Based ModelingProduction PipelinesEvaluation FrameworksHybrid Extraction Systems

Soft Skills

Clear CommunicationCross-Functional CollaborationOwnership Mentality

Tools & Technologies

AI FrameworksDistributed Data PlatformsEvent-Driven ArchitecturesStreaming Systems

Certifications & Qualifications

MS or PhD in Quantitative Field

Industry Keywords

Document AIFinancial Data ModelsProcurementContract ManagementSpend Analytics

Tech Stack

Tools & technologies

AzureKafkaPythonRay

About the role

Key responsibilities & impact

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.
You will 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 and where model accuracy has a contractual SLA.

Requirements

What you’ll need

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

Benefits

Comp & perks

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