Data Scientist

🔥 12 minutes ago

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Terzo

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2020

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

Artificial Intelligence • SaaS • Enterprise

Terzo is an enterprise AI and analytics platform that transforms unstructured business documents (contracts, invoices, POs) into structured, analytics-ready financial and contractual data. It uses hybrid AI (NLP, computer vision, neural networks) plus human QA to extract, normalize, and connect metadata at enterprise scale, powering contract intelligence, spend and financial insights, procurement and legal workflows, and integrations with ERPs, CRM, CLMs, and cloud storage. Terzo positions itself as a B2B, SLA-backed AI-as-a-Service solution for finance, procurement, legal, and supply-chain teams to reduce cost, improve compliance, speed M&A diligence, and surface revenue and savings opportunities.

📋 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. • 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

• 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

• 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