
51 - 200 employees
Founded 2016
🤖 Artificial Intelligence
☁️ SaaS
🏢 Enterprise
Artificial Intelligence • SaaS • Enterprise
IV. AI is an enterprise AI company that builds decision-intelligence and data-orchestration software to turn messy, fragmented datasets into trustworthy, actionable insights. Its platform includes Magnet (data discovery and cleaning), Toaster (modeling, predictions, and anomaly detection), and Scissors (no-code front-end for embedding AI insights into workflows and existing stacks such as CRMs and CDPs). IV. AI emphasizes agentic AI, model traceability and auditability, data governance, and integration with customers' existing infrastructure, and offers solutions for entertainment & media intelligence, market intelligence, location/POI verification, and GEO/AEO commerce signals.
🕒 September 4
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51 - 200 employees
Founded 2016
🤖 Artificial Intelligence
☁️ SaaS
🏢 Enterprise
Artificial Intelligence • SaaS • Enterprise
IV. AI is an enterprise AI company that builds decision-intelligence and data-orchestration software to turn messy, fragmented datasets into trustworthy, actionable insights. Its platform includes Magnet (data discovery and cleaning), Toaster (modeling, predictions, and anomaly detection), and Scissors (no-code front-end for embedding AI insights into workflows and existing stacks such as CRMs and CDPs). IV. AI emphasizes agentic AI, model traceability and auditability, data governance, and integration with customers' existing infrastructure, and offers solutions for entertainment & media intelligence, market intelligence, location/POI verification, and GEO/AEO commerce signals.
• Serve as the technical backbone for the growing data science team • Establish statistical rigor and machine learning methodology across models, metrics, and analyses • Define code review standards, reproducibility practices, and the path from prototype to production • Stay current on modern machine learning and agentic workflow techniques and support relevant adoption • Contribute hands-on to analyses and models • Migrate ad hoc notebooks into centralized, version-controlled, reproducible workflows and shared artifact storage • Design and implement production-grade data pipelines on GCP, including scheduled and triggered jobs • Mentor data scientists across varying experience levels • Partner with the DS team's manager, SVP of Product & Data, on technical direction • Work across multiple projects with varying scope and domain
• 6-8+ years of professional data science experience, with a strong formal foundation in statistics and machine learning • At least 2-3 years of hands-on production/deployment experience • Experience productionalizing data science work, moving from notebooks to scheduled, reliable, production pipelines • Hands-on experience with GCP, including BigQuery, Cloud Composer, Cloud Functions, or similar • Strong Python skills across ETL, analysis, and modeling • Experience mentoring or technically leading less experienced data scientists • Experience thriving in a startup, small, or other 1-10 environment where structure was built from scratch and outcomes were owned • Data engineering background helpful but not required • Experience with agentic or LLM workflows helpful but not required • Data analytics or BI tooling experience helpful but not required • Authorization to work in the US without company sponsorship
Apply Now🕒 September 4
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