YipitData

HQ
New York
470 Total Employees
Year Founded: 2013

YipitData Benefits Overview

Compensation + Benefits

Offers 401(K)

Offers accidental death & dismemberment insurance

Offers life insurance

Offers company equity

Offers employee discounts

Offers dental insurance

Offers health insurance

Offers mental health benefits

Offers dependent care

Offers Flexible Spending Account (FSA)

Offers vision insurance

Offers Health Savings Account (HSA)

Provides family medical leave

Provides fertility benefits

Offers generous parental leave

Company Culture

Provides commuter benefits

Provides free snacks and drinks

Office is pet friendly

Offers legal assistance

Utilizes a flexible work schedule

Offers a remote work program

Work-Life Balance + Wellbeing

Offers company-sponsored outings

Offers gym membership

Offers an Employee Assistance Program (EAP)

Offers generous PTO

Provides paid sick days

Provides paid holidays

Provides bereavement leave

Career Growth + Development

Provides customized development tracks

Job training & conferences

Recently posted jobs

57 Minutes AgoSaved
Remote
US
Big Data • eCommerce
Lead AI product strategy for investor workflows, designing AI-native products (agents, copilots, MCPs, APIs). Partner with investors, engineers, researchers, and designers to translate LLMs, retrieval, and agent architectures into reliable, production-grade features. Drive rapid experimentation, define evaluation and trust frameworks, and shape platform evolution for investor-grade AI products.
4 Hours AgoSaved
Remote
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Big Data • eCommerce
Design end-to-end UX for an AI-powered B2B data product. Translate complex AI and data concepts into intuitive, trustworthy interfaces; conduct user research; define workflows; collaborate with Product, Engineering, and Data Science; contribute to design systems and maintain design quality through launches.
10 Hours AgoSaved
Remote
US
Big Data • eCommerce
Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.