Product Data Scientist, G1 and Photos

GoogleSan Francisco, CA
1d$138,000 - $198,000

About The Position

Photos+G1's mission is to be the home for your life’s content, safely stored and deeply understood, so your personal AI can help you grow, all through a unified Google membership. In this role, you will empower this organizational mission and work closely with Data Engineering, Data Science, and Product Operations and Strategy (Product Operations/Strategy) towards our team's mission to "Understand our Users to drive product change." The tight-knit collaboration between our complementary technical skills and skillsets is our superpower and allows us to set and accomplish an exciting agenda spanning analytical tools, insights, and product strategies. The US base salary range for this full-time position is $138,000-$198,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google [https://careers.google.com/benefits/].

Requirements

  • Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  • 5 years of work experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL) or 2 years work experience with a Master's degree.

Nice To Haves

  • Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.

Responsibilities

  • Partner closely with Finance, User Experience (UX), Product Management (PM), and Engineering to deliver analytics and experimentation (e.g., analytics/experimentation) infrastructure and processes that empower the team to deliver insights and decision-making at scale.
  • Identify business problems where a data science approach is appropriate.
  • Foster a culture of continuous learning within the broader team. Proactively identify and advocate new data sources, tools, and methodologies to enhance our analytical capabilities and drive innovation.
  • Serve as a key conduit of understanding the relationship between Cross-Product Area (XPA) teams, and when necessary, elucidate and quantify tradeoffs between competing goals.
  • Bring structure to the problem, apply the right problem-solving solutions, and recommend actions that improve our user experience, product, and business.
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