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Salary
$69k – $134k per year (Estimated)
Location
In office (San Francisco)
Seniority
Junior · 1+ year exp
Employment
Full-Time
Overview
Company
Impact
Profile match
Hive (Hive Moderation) is a leading cloud-based artificial intelligence and visual intelligence platform headquartered in San Francisco, California. Founded in 2013 by Kevin Guo and Dmitriy Karpman, the company has raised over $85 million in funding (reaching a $2B valuation) to build enterprise machine learning systems powered by a vast, distributed data-labeling workforce.

About Hive

Hive is the leading provider of cloud-based AI solutions to understand, search, and generate content, and is trusted by hundreds of the world's largest and most innovative organizations. The company empowers developers with a portfolio of best-in-class, pre-trained AI models, serving billions of customer API requests every month. Hive also offers turnkey software powered by proprietary AI models and datasets, unlocking breakthrough applications for critical business needs with deep learning and generative AI. Collectively, Hive's technology is transforming approaches to platform integrity / content moderation (including AI-generated content detection), brand protection, sponsorship measurement, context-based ad targeting, and more.

Hive has raised over $120M in capital from leading investors, including General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. We have over 250 employees globally in our San Francisco, Seattle, and Delhi offices. Please reach out if you are interested in joining the future of AI!

Product Analyst - Sports, Media, & Marketing Role

As a Product Analyst on the Hive Media team, you will be a key member of a growing product team and will own business-crucial quantitative analysis. You are a self-starter who works efficiently and is capable of working with engineering to automate analysis to focus on high-value-add projects. As a Product Analyst, you relish tallying up the numbers one minute and communicating your findings the next. You are well versed in verbal, written, and visualization-driven communications. You will work to effectively turn business questions into data analysis, and provide meaningful data backed recommendations to our business teams and clients.

Responsibilities

  • Develop product quality and performance KPIs and track over time via dashboarding and automated reporting
  • Analyze high volume data using SQL and effectively communicate findings
  • Collaborate with Business Development to provide data-backed insights to our partners and clients
  • Develop and own product processes. Collaborate with Engineering and QA teams to execute on process-driven objectives
  • Maintain awareness of industry best practices for data maintenance handling as it relates to your role
  • Adhere to policies, guidelines and procedures pertaining to the protection of information assets
  • Report actual or suspected security and/or policy violations/breaches to an appropriate authority

Requirements

  • You have a bachelor’s or graduate degree in a quantitative field such as Statistics, Computer Science, Engineering, Mathematics
  • You have 1-3 years of work experience in an analytics focused role at a technology or technology consulting firm
  • You have experience scripting with SQL, extracting large sets of data, and designing ETL flows. You also have experience with a statistical or scripting language such as Python, Node JS, R, or MATLAB
  • You have experience in an interdisciplinary/cross-functional field. Added points for enterprise-technology, SaaS, client facing, or start-up experience
  • You have distinctive problem-solving skills and impeccable business judgment. You know how to translate analysis results into business recommendations, and possess excellent written and verbal communication skills
  • You have experience articulating product and business questions, pulling data from datasets (SQL) and conducting business analysis to arrive at an answer. You are able to collaboratively structure and define analysis with initially ambiguous requirements
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